NMR APPLICATIONS IN FOOD ANALYSIS PART B 7_NMR Applic… · 256 Noemi Proietti, Donatella Capitani,...

In: Analytical Chemistry ISBN: 978-1-53612-267-1

Editors: Marcello Locatelli and Christian Celia © 2017 Nova Science Publishers, Inc.

Chapter 7

NMR APPLICATIONS IN FOOD ANALYSIS: PART B

Noemi Proietti1, Donatella Capitani1,1 , Violetta Aru2,

Alessia Bellomaria3, Fabio Bertocchi3, Bruno Botta4,

Laura Ruth Cagliani5, Augusta Caligiani6, Francesco Capozzi7,

Dorisa Çela3, Flaminia Cesare Marincola8, Alessandra Ciampa9,

Laura Del Coco10, Roberto Consonni5, Carmelo Corsaro11,

Maurizio Delfini12, Francesco Paolo Fanizzi10, Vito Gallo13,

Francesca Ghirga14, Raffaella Gianferri12, Chiara Roberta Girelli10,

Cinzia Ingallina4, Luca Laghi7, Mario Latronico13,

Francesco Longobardi15, Claudio Luchinat16,

Domenico Mallamace17, Stefano Mammi18, Walter Mandaliti3,

Luisa Mannina1,4, Federico Marini12, Pietro Mastrorilli13,

Pierluigi Mazzei19, Alfredo Miccheli12, Alessandra Micozzi20,

Salvatore Milone20, Adele Mucci21, Ridvan Nepravishta3,

Maurizio Paci3, Angelica Palisi22, Anatoly Petrovich Sobolev1,

Alessandro Piccolo19, Gianfranco Picone7, Antonio Randazzo23,

Valeria Righi24, Archimede Rotondo25, Andrea Salvo25,

Francesco Savorani26, Paola Scano5,8, Elisabetta Schievano18,

Fabio Sciubba12, Leonardo Tenori27, Alessia Trimigno7,

Paola Turano16, Sebastiano Vasi28 and Valeria Di Tullio1 1Laboratorio di Risonanza Magnetica “Annalaura Segre,”

Istituto di Metodologie Chimiche, CNR, Monterotondo (Rome), Italy

1 Corresponding Author Email: [email protected].

Complimentary Contributor Copy

Noemi Proietti, Donatella Capitani, Violetta Aru et al. 256

2Chemometrics & Analytical Technology, Department of Food Science,

University of Copenhagen, Copenhagen, Denmark 3Dipartimento di Scienze e Tecnologie Chimiche,

Università di Roma “Tor Vergata,” Rome, Italy 4Dipartimento di Chimica e Tecnologie del Farmaco,

Sapienza Università di Roma, Rome, Italy 5Istituto per lo Studio delle Macromolecole, Lab NMR, CNR, Milan, Italy

6Dipartimento di Scienze degli Alimenti,

Università degli Studi di Parma, Parma, Italy 7Dipartimento di Scienze e Tecnologie Agro-Alimentari,

Università di Bologna, Cesena, Italy 8Dipartimento di Scienze Chimiche e Geologiche,

Università di Cagliari, Monserrato (Cagliari), Italy 9Consiglio per la Ricerca in Agricoltura e l’Analisi

dell’Economia Agraria – Centro di Ricerca per lo Studio

delle Relazioni tra Pianta e Suolo (CREA-RPS), Rome, Italy 10Dipartimento di Scienze e Tecnologie Biologiche ed Ambientali,

Università del Salento, Campus Ecotekne, Lecce, Italy 11CNR-IPCF, Istituto per i Processi Chimico-Fisici del

CNR di Messina, Messina, Italy 12Dipartimento di Chimica, Sapienza Università di Roma, Rome, Italy

13Dipartimento di Ingegneria Civile, Ambientale, del Territorio,

Edile e di Chimica (DICATECh), Politecnico di Bari, Bari, Italy 14Italian Institute of Technologies, @CNLS Sapienza, Rome, Italy

15Dipartimento di Chimica, Università degli Studi di Bari

“Aldo Moro,” Bari, Italy 16Centro di Ricerca di Risonanze Magnetiche CERM,

Università di Firenze, Sesto Fiorentino (Florence), Italy 17Consorzio Interuniversitario per lo Sviluppo dei Sistemi a

Grande Interfase - CSGI, Sesto Fiorentino (Florence), Italy 18Dipartimento di Scienze Chimiche, Università degli

Studi di Padova, Padova, Italy 19Centro Interdipartimentale per la Risonanza Magnetica

Nucleare per l’Ambiente, l’Agro-Alimentare ed i Nuovi Materiali (CERMANU),

Università di Napoli Federico II, Portici (Naples), Italy 20Istituto Zooprofilattico Sperimentale dell'Abruzzo e del

Molise “G. Caporale,” Teramo, Italy 21Dipartimento di Scienze Chimiche e Geologiche,

Università di Modena e Reggio Emilia, Modena, Italy 22Dipartimento di Farmacia, Università degli Studi di Salerno, Salerno, Italy 23Dipartimento di Farmacia, Università di Napoli Federico II, Naples, Italy


NMR Applications in Food Analysis: Part B 257

24Dipartimento di Scienze per la Qualità della Vita,

Università di Bologna, Rimini, Italy 25Dipartimento di Scienze Biomediche, Odontoiatriche e delle Immagini

Morfologiche e Funzionali, Università degli Studi di Messina, Messina, Italy 26Dipartimento di Scienza Applicata e Tecnologia,

Politecnico di Torino, Tourin, Italy 27Dipartimento di Medicina Sperimentale e Clinica,

Università di Firenze, Florence, Italy 28Dipartimento MIFT, sezione di Fisica, Università di Messina, Messina, Italy

ABSTRACT

Applications of low-field NMR relaxometry and NMR-imaging in the analysis of food

samples are described using examples of different food matrices and different problems

related to food processing, maturation and ageing, authenticity, shelf-life, perishability, etc.

Keywords: low-field NMR relaxometry, NMR-imaging, food science, food composition

1. INTRODUCTION

“NMR Methodologies in Food Analysis” discusses some fundamental aspects of NMR

methodologies in food science. Both Parts A and B are dedicated to the most relevant state-

of-the-art practical applications of NMR in food science. The examples reported are chosen

by members of the Italian Group of Magnetic Resonance in Food Science actively involved

in the development of new NMR methodologies to study food matrices using various NMR

approaches. In this chapter, applications of low-field NMR relaxometry and NMR-imaging

in the analysis of food samples are reported using examples of different food matrices and

different problems related to food quality processing, maturation and ageing, authenticity,

shelf-life, perishability, etc.

2. LOW FIELD NMR APPLICATIONS

Low field 1H NMR relaxometry is a suitable tool to study the most abundant

components of intact foodstuffs from measurements of relaxation parameters and

amplitudes of the NMR signals. Information on food microstructure such as water

compartments and diffusion can be obtained by detecting proton signals dominated by H2O



contained in foodstuffs. This technique does not require sample pretreatment and once

developed can be easily used to quality control applications.

Information at the microns level can be obtained by NMR, based on longitudinal

relaxation time (T1), water self-diffusion (Dw) or T2. T2-weighted signals are undoubtedly

those most often employed (Colnago et al., 2015), analyzed by fitting procedures of the so

obtained curve, in order to describe separately the physical food compartments that

contributed to them. The following two examples allow appreciating the power of these

protocols. Even if muscle fibers are characterized by a diameter as thin as a few tens of

microns, it is nowadays routinely possible to observe and study separately intra and extra

myofibrillar water (Petracci et al., 2014). Again, even if the pericarp cell of a kiwifruit has

a diameter of about 100 microns only, the signal due to vacuole, cytoplasm and intercellular

space can be observed separately (Tylewicz et al., 2011).

Historically, the main obstacle in the so described application of NMR relaxation has

been the ascription of the T2 weighted signals to specific compartments of the samples

under investigation. As the decay of the NMR signal due to T2 is exponential, the most

intuitive way to do so consists in its fitting towards the sum of a handful of exponential

curves. This has been done successfully in many cases, among which those just mentioned

for meat (Petracci et al., 2014) and kiwifruit (Tylewicz et al., 2011), or the one of albumen,

in which the fluid and thick portions can be separately observed (Laghi et al., 2005). There

are several reasons to consider this procedure to extract useful information from T2

weighted signals as sub-optimal. One of them is that describing the signal of water in a

certain compartment with a single exponential curve implies that the state of water is

perfectly homogeneous across the entire sample consider, which can be a too gross

approximation.

In order to overall the limits intrinsic to the fitting towards a discrete number of

exponential curves, several researchers attempted to present T2 decays as T2 quasi-

continuous distribution data (Gao et al., 2016), similarly to the high-resolution spectra

counterparts. In order to do so, Laplace inversion is used. Unfortunately, in the presence of

finite and/or noisy data, such inversion is ill posed, i.e., it does not give rise to a unique

solution. In order to overcome the impasse, several regularization methods have been

described, with the aim of finding a single solution, which could be considered as the

“best,” according to some criteria (Borgia et al., 1998). Prange and Song tried to find an

out-of-the-box solution to the task of finding the best regularization method to Laplace

inversion, by actually giving up looking for the “best” T2 distribution (Prange & Song,

2009). At its place, they setup a Monte Carlo algorithm generating probable solutions, from

which the statistical properties of the solutions themselves could be analyzed. Interestingly,

they found that the mean solution spectrum was smooth and close to the regularized

solution, even when individual solutions could be spiky. Zou et al. faced from an again

different perspective the problem of the Laplace inversion of T2 weighted signals, because

they focused on the uncertainty introduced in the T2 distribution generated, by applying a



frequentist method (Zou et al., 2016). Echo and T2 distribution amplitudes on one side, T2

distribution slopes and curvatures on the other served as objective basis for the purpose.

Signal-to-noise ratio was found to be the characteristic of T2 decays mainly affecting the

uncertainty of the T2 distribution, while norm-smoothing method performed better than

curvature smoothing.

In order to extract useful information from T2 weighted signals by avoiding the

limitations of discrete fitting and quasi-continuous T2 distribution calculation, researchers

have tried to rely on multivariate analysis. This choice, not based on a priori outlined

model, has been attempted in particular in cases of NMR applications described as

dynamic, where NMR observations are performed recursively in order to follow the

progressive changes a food undergoes along processing or storage. Curiously, even if the

extraction of each piece of relevant information by multivariate analysis seems a very

powerful protocol for the elaboration of relaxation time signals, the idea has been exploited

only rarely in the past. Three cases are worth mentioning, in this context. Engelsen et al.

looked for the consequences of baking on breadcrumb, by simulating baking inside a TD-

NMR instrument (Engelsen et al., 2001). In order to highlight the main changes in water

state related to core temperature increase, they analyzed the T2 weighted signals by means

of principal component analysis (PCA). They found two abrupt changes in the water state

of breadcrumb, ascribed to the onset and to the end of gelatinization, respectively. In

parallel, the researchers managed to relate water state, observed by TD-NMR, to bread

texture changes upon staling. In order to do so, they applied successfully a further

chemometric analysis, partial least square regression (PLSR). Micklander et al. looked for

the main transitions of state meat water undergoes upon cooking (Micklander et al., 2002).

In order to do so, they simulated cooking process directly inside a TD-NMR instrument,

operating at 23.2 MHz, and performed data reduction of the so obtained T2-weighted curves

by beans of PCA. While the first component of this multivariate model mainly incorporated

the effect of temperature increase, the second and third showed abrupt changes that were

ascribed to the denaturation of sarcoplasmic proteins and to the shrinkage of the myofibrils.

Laghi et al. simulated in vitro the digestion of Parmigiano-Reggiano cheese, which selling

strategy often revolves around the high digestibility of its protein fraction. In order to

characterize the amount of protein fraction liberated, together with its overall profile, they

performed the digestion on two samples at different ripening levels and highlighted the

differences among the digestates from the two by PCA, applied to centered unscaled T2

weighted signals, obtained at 20 MHz on a Bruker “The Minispec” benchtop instrument

(Laghi et al., 2013).

In very recent years the group held by Luiz Alberto Colnago, operating at the Brazilian

Agricultural Research Corporation, has operated an authentic revamping of the idea, by

publishing a series of works that seem to have the proper characteristics to become

milestones if the field. In one of them (Pereira et al., 2013), the total soluble solid content

of plums was measured by means of a refractometer. In parallel T2-weighted signals have



been registered at 0.23T on intact fruits. Soft independent modeling of class analogy

(SIMCA), performed on NMR data reduced by PCA, was able to assign the plums to two

classes according to their low (9 to 12%) or high (13 to 22%) soluble solids content, with

a 96 accuracy. In a conceptually similar paper (Pereira & Colnago, 2012), water content

was registered by gravimetric method on samples of beef and then accurately predicted by

T2-weighted NMR signals, again at 0.23T, by means of multivariate analysis. In the

specific case, partial least squares (PLS) and principal component regression (PCR)

showed comparable prediction ability.

T1 and Dw have been far less employed for food microstructure elucidation than T2,

with a few exceptions (Tylewicz et al., 2016; Santagapita et al., 2013) because the former

requires longer acquisition times and the latter often needs poorly parsimonious models for

interpretation. This state of art can be foreseen to change substantially due to Colnago work

(Colnago et al., 2015), which described a fast protocol to acquire T1 and T2 relaxation

signals simultaneously, with the TD-NMR instruments available at present.

2.1. Fruits and Vegetables

Horticultural products are challenging food with respect to maintaining quality during

the food chain between field and consumer. Consumer expects its fruit and vegetables to

be of consistent high quality, at optimum ripeness, juiciness and texture, and free from

internal and external quality defects. Whether, harvesting and storage conditions affect the

quality of fruit and vegetables. Off-season fruits coming out of long term cold storage show

particularly high degree of quality variation due to a progressive development of fungal

infection, over ripeness and bruises. For this reason, most fruits and vegetables need to be

examined and selected before to be transported, processed or sold. Therefore, a strong need

exists for rapid and cost-effective quality control tools. Low field proton NMR relaxometry

has become an important tool in quality control analysis of agri-food products. In particular

relaxation times (T1 and T2) and molecular self-diffusion coefficient (D) NMR

measurements have been employed for studying the dynamic of water molecules as well

as the subcellular water distribution within plant tissues and water molecule transport

properties in sub-cellular compartments (Callaghan et al., 1979; Hills & Belton, 1989;

Snaar & Van As, 1992). The role and state of water in food is generally agreed to be of

importance in a range of properties such as texture, microbial growth rates, storage,

deterioration, etc (Duckworth, 1975; Belton, 1984; Hills et al., 1990). Changes in water

NMR dynamics during ripening, processing and storage operations on fruits and vegetables

can, in principle, reveal sub cellular modifications and contribute to a microscopic

understanding of these processes (Gil et al., 1996).

Low-field NMR relaxometry and diffusometry have been applied in the quality

inspection of fruits, and to monitor the fruit maturity and the effects of processing on



morphology. Apple has been one of the most studied fruits. The parenchyma tissue of apple

is representative of multicompartment cellular tissue that has been much studied by one

dimensional relaxation techniques (Hills & Clark, 2003). Four relaxation peaks can be seen

and assigned to the different cell compartments (Snaar & Van As, 1992) (Figure 1).

Figure 1. Distribution of transverse water proton relaxation times in fresh ‘Red Delicious’ apple tissue

measured at spectrometer frequency of 23.4MHz with a CPMG 90–180° pulse spacing of 200 μs.

Numbers refers to relaxation peaks of the water proton in the different cell compartments: 1, the vacuole;

2 and 3, cytoplasm and extra-cellular compartment and 4, cell wall. With the permission of reference

Marigheto et al., 2008.

The peak with shortest T2 (peak 4) probably arises from more rigid components of the

cell walls in the apple matrix. Peaks 3 and 2 can be associated with the water in the

cytoplasm and extracellular compartments. The peak with the longest T2 (peak 1) is

associated with the water in the vacuole. Diffusion of water between the various subcellular

(vacuolar, cytoplasmic) and extra-cellular water compartments which averages the

magnetization to an extent that depends on cell morphology and membrane permeability.

This aspect complicates the peak assignment.

Keener et al. have investigated the relationship between low-field (0.13 T; 5.4 MHz)

apparent water proton diffusion coefficients (Dw), the dominant CPMG T2 measured with

a pulse spacing of 2 ms, and degree of ripeness (soluble solids content) as measured by

refractometry (Keener et al., 1999).

The following trends were reported:

a) In healthy “Golden Delicious” and “Granny Smith” apples, the T2 decreased with

increasing soluble solid content (Brix) in healthy apples.



b) The water diffusivity decreased with increasing soluble solid content in all

samples, both bruised and healthy. This is no doubt a result of the increasing

viscosity of more concentrated sugar solutions.

c) Internal defects caused by bruising, watercore, and internal browning resulted in a

decrease in T2 in all varieties at this low field strength.

Although the decrease in T2 with increasing sugar concentration might be expected

because the same trend is observed in simple sugar solutions, the situation in apple tissue

is rather more complicated. Ripening is associated with hydrolysis of starch granules which

causes an increase of sugar concentration. However, starch granules themselves behave as

relaxation sinks because they compromise semi-crystalline amylopectin and an amylose

matrix and there is fast exchange of water and starch hydroxyl protons.

Cho et al. reported that the amplitude of the sugar proton peak following water

suppression with a T1 null sequence correlated well with the sugar content in apples (as

well as banana and muskmelon) (Cho et al., 1991). This exploited the observation that the

T1 of exchangeable water protons was at least twice as long as that of the non-exchanging

sugar protons, so a simple inversion recovery sequence with a delay time chosen to null

the water signal, leaves a substantial positive sugar proton signal.

Mealiness is common quality problem in apples that can arise during long-term storage

and is associated with cell wall adhesion. Intercellular adhesion is strong in a healthy apple

so that chewing causes immediate cell rupture and liberation of the juice in the mouth. On

the other hand, the intercellular adhesion is weak in mealy apple so that chewing merely

causes separation of intact cells but not rupture, resulting in an unpleasant sensation akin

to eating a dry powder rather than apple. NMR T2 can be used to distinguish mealy and

fresh apples. The first significant measurements and mealiness were reported by Barreiro

et al. who found that one-dimensional T2 distribution for mealy apple was skewed to shorter

relaxation times with a significant tail located in the maximum T2 range (Barreiro et al.,

1999; Barreiro et al., 2000). In contrast non-mealy apples had normal non-skewed T2

histograms (Figure 2).

While this observation is interesting, it is unfortunately a little practical relevance for

on line detection of mealiness because the measurement was undertaken using a high field

imaging system operating at 4.65 T. More recently the potential of multi-dimensional

relaxation experiments has been used for exploring the relationship between water

compartmentation and fruit/vegetable quality. Figure 3 shows the T1–T2 cross correlation

spectrum for parenchyma tissue of fresh and mealy apple (Hills, 2008; Marigheto et al.,

2008). The dependence of the peak positions and intensities on physiological state can be

considered probes of mealiness (Hills, 2008). A comparison with the fresh apple spectra

suggests that the mealy condition lead to T1 and T2 increase. In particular, T1 of peak

associated with the cell wall (labeled as peak 4) in mealy apples is much longer that of

fresh apples (Figure 3). This phenomenon is associated with the changes in the water



distribution between the cell compartment and it could be exploited in the development of

online NMR sensors of fruit quality.

Figure 2. Examples of mealy apple (first and second line, the first one also showing internal breakdown)

and intermediate and fresh fruits (third and fourth line respectively). With the permission of reference

Barreiro et al., 2000.



Figure 3. Experimental T1–T2 cross correlation spectra of apple tissue measured at 23.4MHz with a

CPMG 90–180° pulse spacing of 200 s. (a) Tissue from fresh apple (b) tissue from mealy apple. 1,

vacuole; 2 and 3, cytoplasm and extracellular compartment; 4, cell wall; 5, starch; 6, starch or protein or

vascular tissue; 7, starch; 8, pectin; 9, baseline artifact and 10, exchange peak. The diagonal line denotes

T1 = T2. With the permission of reference Marigheto et al., 2008.

Several 1D and 2D relaxation and diffusion protocols were also tested to find the most

sensitive technique for the detection of mealiness and ripening in apples (Barreiro et al.,

1999; Barreiro et al., 2002). The loss of membrane integrity upon internal browning in

apple and pear tissues was manifested by changes in relaxation time distributions in T2–T1

and T2–D correlation measurements (Hernandez-Sanchez et al., 2007).

Low field NMR relaxometry and diffusometry have been applied to assess internal

browning and watercore in apples. Internal browning disorder manifests itself as light and

dark brown patches throughout the cortex and core. The disorder is induced by high CO2

concentrations, especially during storage in modified atmospheres, but canal so appear in

unpicked fruit on the tree. Watercore is a physiological disorder affecting apples in which

intercellular air spaces in the flesh adjacent to the vasculature become filled with fluid

having elevated sorbitol and sucrose concentrations (Simons, 1968; Bowen & Watkins,

1997; Kumpoun et al., 2003). Although watercore may disappear during storage in some

apple varieties, it can develop into a severe form of internal browning in others. Currently,

apples with internal browning cannot easily be sorted from good apples when the defect

does not affect their external appearance. In general, lots with excessive levels of internal

browning cannot be packed for the fresh market. In apple varieties where watercore causes

problems during storage, non destructive detection of watercore would allow defective

apples to be sorted out and marketed before the unaffected apples are placed in storage.

Although internal browning or watercore had no effect on the self-diffusion coefficient

of water, differences in T2 values between healthy and defective apple tissue were

observed. Jung et al. evaluated the use of a low-field (0.1256 T) 1H NMR sensor for

detection of two types of internal disorder found in apples (Jung et al., 1998). They

conducted Carr–Purcell–Meiboom–Gill (CPMG) T2 tests on whole apples that were



healthy or that had either internal browning or watercore. The T2 value of the apples with

internal browning decreased as the severity of internal browning increased. By contrast,

the T2 values of the apples with watercore increased as the severity of watercore increased.

The authors concluded that T2 measurement for detection of apples with severe levels of

internal browning should be possible. Recently Chayaprasert and Stroshine developed a

rapid sensing based on low-cost low-field proton magnetic resonance sensor was to assess

internal browning in whole apples (Chayaprasert & Stroshine, 2005). The MR sensing

system consisted of a permanent magnet equipped with a conveyor belt. Although the T2

relaxation curves were affected by the apple’s motion, differences between apparently

healthy apples and those with internal browning could be distinguished using apparent T2

values. A similar sensor was used by Cho et al. for on-line detection of internal browning

and watercore in apples (Cho et al., 2008). Changes in T2 components associated with

different water compartments in affected apples could be associated with a movement of

water from vacuoles into cytoplasm and extracellular spaces (Cho et al., 2008).

Avocado fruits ripen only after picking and, therefore should be harvested when

mature. Unfortunately, there are no visible external changes in the fruit to indicate maturity

but it has been shown that increasing oil content and decreasing water content correlate

closely with sensory measures of maturity and that the oil content correlates well with the

increasing dry weight during maturation. The relationship between NMR parameters and

oil content and /or dry weight in avocado has been investigated by MR techniques (Chen,

1993). Working at a high-filed of 2T (85 MHz) Chen and co-workers showed that T1 and

T2 of e water decrease linearly with increasing dry weight with satisfactory correlation

coefficients.

Besides standard T2 CPMG and T1 methods, more sophisticated relaxation and

diffusion NMR techniques were also tested to assess feasibility of fast online assessment

of quality factors such as maturity, oil content and presence of hard lumps in avocado

(Marigheto et al., 2005). Two-dimensional T2–T1 correlation measurements proved to be

adequate for determination of oil content in avocado tissue, but the long acquisition time

excluded it from online implementation. They are therefore the preferred method for

noninvasive off-line quality control.

Effects of banana ripening during storage were studied by relaxometry (CPMG),

diffusometry (PFGSE) and a combination of (PFGMSE-CPMG) investigated by Raffo and

coworkers (Raffo et al., 2005). Following the methodology of earlier work on apple and

potato tissue the CPMG echo decay for banana were deconvoluted into three components

corresponding to cellular, cytoplasmic and vacuolar water (Hills & Le Floch, 1994; Hills

& Remigerau, 1997). It was then found that the cytoplasmic and vacuolar water T2 showed

significant increases with storage (up to 7 days). The changes were explained as the

progressive enzymatic hydrolysis of starch granules during ripening. Moreover, the

observed water self-diffusion coefficient decrease is related to sugar accumulations as

starch hydrolysis proceeds.



Internal browning in pears has been studied T1-T2 correlation spectroscopy at a proton

frequency of 300.15 MHz. Affected tissue has a shorter transverse relaxation rate compared

to healthy tissue especially at higher magnetic field strength. Tissue disintegration as well

as water evaporation appeared to be the main reason for this difference. Like apple the T2-

D spectra of fresh pears and pears with internal browning both showed two peaks

corresponding to water in the vacuole and from cytoplasm. In all cases examined vacuolar

T2 and its diffusion coefficient of the internally browned pear are longer than those

measured in fresh pear (Hills, 2006).

Kiwifruit is another fruit that has been much studied. Many study performed by MRI

have been focused on the changes in relaxation time and diffusion maps in kiwifruits as

they ripened (Clark et al., 1998a). Measurements were made at 2T (85MHz) and T1 and T2

maps were reported over a 30-days ripening period. It was shown that all relaxation times

show significant increase during ripening, especially in the flesh and locule regions. TD-

NMR has also found widespread use in mechanistic studies on microstructural effects of

processing routes used to minimize food degradation such as air-drying, freeze-drying and

osmotic drying (Van Duynhoven et al., 2009). Osmotic dehydration (OD) is a widely used

method to partially remove water from fruits by immersion of cellular tissue in hypertonic

aqueous sugar solutions. This process is particularly common as a pre-treatment before air-

drying or to obtain minimally processed fruit and vegetables products. In the case of

kiwifruit, the OD is employed to increase the product shelf life, but since by itself is not

enough adequate, generally it is combined with other stronger stabilizing methods such as

freezing or air-, freeze-, vacuum-drying (Rahman & Conrad, 2007). The combined

application of T2 and water self-diffusion coefficient measurements allowed having an

insight of the changes in the cellular compartments of kiwifruit’s outer pericarp promoted

by OD. T2 measurements enabled the quantification of the protons located in the three main

cell compartments within the mobile water is located: vacuole, cytoplasm plus extracellular

space, and cell wall. Water self-diffusion coefficient evaluated through a single component

model was found to be a fast technique to follow OD treatment consequences along time

and to highlight the different response to the treatment from fruits of different ripeness.

Low Field NMR results showed that the OD process influenced water mobility

characteristics within the vacuole and cytoplasm plus extracellular space compartments.

The possibility to highlight how the response to OD treatment was modulated by fruit

ripeness suggests that low field NMR can represent a powerful and versatile tool to

investigate the behavior of vegetable tissues during minimal processing (Santagapita et al.,

2013).

Dry matter content is an important quality parameter for many fruits and vegetables

and it has been used to determine in a non-invasive manner by multi-variate modeling of

CPMG decays of potatoes (Thybo et al., 2003). Multi-variate models based on CPMG

decays obtained from raw potatoes successfully predicted the sensory texture (Thybo et al.,

2000) and other quality parameters of cooked potatoes (Povlsen, 2003).



TD-NMR relaxometry was used to study air-drying (Marques et al., 1991a; Marques

et al., 1991b) and freeze-drying of carrots (Hills & Nott, 1999).

Relaxometry could detect sublimation of the frozen core and removal of non frozen

water during freeze-drying. Also anomalous freeze-drying of potato could be detected.

These studies were followed by more sophisticated approaches where the concepts of non-

freezing water and three-compartment relaxation/diffusion models were deployed. CPMG

relaxation measurements localized non-freezing water in cellular tissue of potato in cell

walls and starch granules. T2 relaxation measurements were used to study changes in sub-

cellular water compartmentation and cell membrane integrity after applying air-drying,

freeze-drying, freeze–thaw processing and rehydration in carrot parenchyma tissue (Hills

& Nott, 1999), which, unlike potato (Hills & Lefloch, 1994) and apples (Hills &

Remigereau, 1997) does not contain large numbers of starch granules or intercellular air

gaps. Carrot tissue distinguishes itself due to the high levels of dissolved sugar in the

vacuole and this strongly affects relaxation behavior during drying and freezing. The

observed decrease in relaxation times during drying was a consequence of vacuolar

shrinkage and the progressive concentration of vacuolar sugars.

2.2. Time Domain NMR Spectroscopy in Diary Products Studies

Time Domain (TD) NMR is being used throughout all food science and technology

areas since it is a powerful analytical technique employable to investigate the physical and

chemical properties and texture of many substances of agricultural relevance. TD NMR is

implemented on cost-effective and easy-to-use bench top equipment and does not require

pre-treatment or substantial changes (procedures of extraction, solubilization, etc.) of the

samples.

A wide range of TD NMR applications based on FID analysis, relaxometry and self-

diffusion coefficients determinations have been developed in the research area to cover all

food supply chain and also applied to food industry, in research and development but also

quality and control process). Notwithstanding a relatively few published academic works,

TD NMR represents an excellent alternative to some traditional methods of food analysis

(Hills, 2006).

TD NMR is particularly useful as a tool to study dairy products. In particular, it has

been used to characterize the fat and water in cheese, by NMR signal analysis, the fat and

moisture content, by FID-spin-echo application, as well as the diffusion domains and their

distribution, by relaxometry determinations. (Mariette & Lucas, 2005; Brosio & Gianferri,

2011) In these ways, it is also able to investigate the cheese texture attributes by water state

and distribution study (by relaxometry and diffusion measurements), and furthermore their

change as a function of the ripening, shelf-life or production processes, providing a rapid

method for assessment of cheeses quality. (Brosio & Gianferri, 2011)



Since cheese matrix is extremely complex, several studies have been carried out

(Gianferri et al., 2007a; Gianferri et al., 2007b; Castell-Palou et al., 2011) in order to

developed models to evaluate macroscopic physical parameters by T1 and T2 relaxation

times measurements, while auto-diffusion coefficients do not require any interpretive

model (Brosio et al., 2008; Brosio & Gianferri, 2011).

Relaxation measurements (by Carr-Purcell-Meiboom-Gill pulse sequence) represent

most widespread TD NMR application, but they require adequate data-analysis to obtained

structural and compositional information. In fact, in heterogeneous food systems different

NMR relaxation times can be measured due to presence more abundant components, i.e.,

water and lipids, as well as due to presence of pores or fat globules, that are different

structural elements. Furthermore, water relaxation is affected by the cheese texture, which

can be related to interactions between water and macromolecules. Therefore, not only so

translation and rotation of water molecules surely affect relaxation, but also diffusive and

chemical exchange processes between water molecules and biopolymers strongly

contribute to T2 and T1 values.

In cheese, the change of dynamic NMR parameters (T1, T2 and self-diffusion

coefficient), as well as the FID amplitude and intensity, can also be correlated with the

system evolution due to a ripening process, as in the case of Grana Padano cheese (De

Angelis Curtis et al., 2000), or an aging process in the shelf-life, as is the case of Mozzarella

di Bufala Campana cheese (Gianferri et al., 2007b).

Moreover, T2 and T1 value changes can be associated with system modification due to

temperature processing or water sorption as during the storage (Castell-Palou et al., 2011).

2.2.1. Ripening of Cheese

Ripening is fundamental, and critical, in the hard cheeses processing. It influences the

sensorial properties (i.e., flavor, texture, color and similar properties) and, since ripe

products require long production, also their monetary value.

Grana Padano and Parmigiano Reggiano are among the most popular PDO cheeses

of Italy and are among of hard cheeses most consumed in the world. Therefore of particular

interest for their production are the De Angelis Curtis et al. work about Grana Padano (De

Angelis Curtis et al., 2000) and that of Bordini et al. on Parmigiano Reggiano (Bordini et

al., 2011).

The relaxation data of the Grana Padano cheese, as function to ripening time, have

been shown a gradual lowering from month 6 to month 18 of the entrapped water T2 values,

indicating that water molecules trapped within junction zones of the like-gel casein

structure – as an integral part of the protein structure –, experience substantial differences,

at least in the sampled stage. Furthermore, as concerning transverse relaxation signal

percentages, the junction zones water amounts increases during the Grana Padano cheese

ripening process, while entrapped water decreases, indicating that casein micelles



shrinkage and lets out water molecules (entrapped water) that, in turn, becomes

“interstitial” water (junction zones water) (De Angelis Curtis et al., 2000).

Bordini et al. have explored two different kinds of Parmigiano Reggiano (aged 15 and

30 months) at different stages of protein hydrolysis process by TD- and HR-NMR (Bordini

et al., 2011). TD-NMR has pointed out that cheeses with different aging times, although

starting from distinct initial compositions, conclude digestion in a similar way, in terms of

free amino acids and small organic compounds, but evolve with different kinetics of

hydrolysis and peptide formation, discriminating the young from the old cheese.

In both studies, the TD NMR spectroscopy has been employed to provide information

about water distribution in hard cheeses and ripening processes. Even if it has to be

emphasized that the ripening process implies many modifications of high complexity, these

approaches provide a way to verify the effect of the ripening process on foodstuffs.

2.2.2. Shelf Life of Cheese

Shelf life of cheese, especially fresh ones, is an important parameter to define the time

before cheese is considered unsuitable for consumption, because it is still safe but its

optimal quality is no longer guaranteed. Hinrichs et al. have detected changes of the water-

holding capacity of different treated fresh cheese by classical TD NMR and the so-called

wash-out-test (Hinrichs et al., 2004). So, authors have shown that good synergetic

properties seem to be correlated with a softer mechanical consistency of the products.

Mozzarella di Bufala cheese is a “pasta filata” (fresh and stretched curd) cheese,

obtained only from buffalo milk in Southern Italy. Changes in microstructure of Mozzarella

cheese during storage and the short shelf-life, suggest that the proteins are not in a quiescent

state immediately after stretching and moulding, but undergo a continuous structural

rearrangement.

In this fresh cheese, the variation in relaxation time values has been correlated with the

system evolution due to aging process in the shelf-life. In particular, serum water T2 values

have shown a notable and gradual lowering from day 1 to day 7, until a constant limiting

value relatable to shelf-life (Gianferri et al., 2007b). Further, always in Mozzarella di

Bufala Campana cheese also some variations in the amino acid profile (by high resolution

NMR spectroscopy) can be correlated with the water and lipid mobility assessed (by TD-

NMR).

Although being extremely effective, the TD NMR potentiality to investigate cheese

state and evolution has not been very investigated in dairy science. Probably, because it

requires an interpretative model of relaxation data (the chemical and diffusive exchange

model) (Brosio et al, 2008) that is not always easy to apply in that it calls for the knowledge

of terms that are difficult to be obtained experimentally. However, by the analysis of proton

relaxation curves, different water population in cheese systems can be observed and they

can provide the possibility of a deeper insight about water dynamics and distribution in

dairy cheese.



2.3. Application of Single-Side NMR Sensor to Food Matrices

Analysis and quality control of food is an important application area for low field time

domain NMR (McCarthy et al., 2006).

This is primarily due to their relatively low cost, ease of operation, ability to provide

information on product composition within short turnaround times. However, as samples

need to be withdrawn, packaged food, intact plants, and process control are largely

excluded from such an analysis unless strategies related to imaging are employed. Progress

in this direction has been made by development of unilateral NMR sensors, devices that

are able to perform non invasive analysis of foods without needing to cut the sample into

pieces that must fit in the NMR tube.

Interesting applications of portable single sided NMR sensors to food matrices have

been reported in literature (Mitchell et al., 2014; Blümich et al., 2008).

One of the first applications of single sided NMR devices in food was the quantitative

study of the oil and water content in food.

Pedersen et al. investigated the performance of conventional benchtop NMR and

single-sided NMR applied to the study of oil-in-water emulsions (Pedersen et al., 2003).

The study demonstrated how CPMG-like pulse sequence can be used on the NMR MOUSE

to obtain quantitative measurements on model food systems. The results were compared to

those obtained using a conventional low field NMR instrument. In a homogeneous field

the decay rate of pure water obtained after applying a CPMG sequence is much longer than

that of oil, which means that the relaxation behavior of the two components is very

different. In a single side NMR sensor, the presence of a strong magnetic field gradient

heavily modifies the decay of magnetization; in fact, the decay rate of water is much faster

than that of oil because water is much more affected by diffusion than oil. This diffusion

weight makes the single side sensor a suitable instrument for quantifying the oil/water

ratios. The trend in relaxation behavior with increasing oil content measured by single-

sided NMR was found to be the reverse of that obtained by homogeneous benchtop NMR.

The reverse of the trend in the decay rates with decreasing the oil content is also foreseeable

as a result of the strong magnetic field gradient which affects the apparent transverse

relaxation time of water due to its rapid diffusion rate. Authors demonstrated that NMR

decays obtained by both homogenous benchtop NMR and single-sided NMR can be readily

deconvoluted in two components in the case of oil-in-water emulsions with oil content

ranging from 10 to 67%.

Furthermore, single-sided NMR was used to obtain compositional information in a

through-package manner, so the product could be analyzed in sealed conditions. For

example, Guthausen et al. investigated the applicability of a single-sided NMR sensor to

measure the fat content in packaged food products (Guthausen et al., 2004). Fat content is

one of the important parameters of quality control in many food products. In this work two

different low-field NMR methods, namely, a ratio method and a relaxation time method,



were applied and discussed. The processed NMR signal was linearly correlated with the

fat content obtained by reference methods. The linear correlation allowed the application

of single-sided NMR for fat measurements. At this aim two methods were applied

(Guthausen et al., 2006). One is based on differences in relaxation times which depend on

molecular mobility and, consequently on the molecular structure (relaxation time method),

whereas the other one exploits the fact that the diffusion coefficients of water and fat differ

by more than an order of magnitude (diffusion weighted method). A pulse sequence was

purposely developed for measuring fat and water content in packaged coffee creams and

packaged mayonnaises and margarines. The processed NMR signal was linearly correlated

with the fat content obtained by reference methods.

In this case the ratio of the final echoes and the first few echoes were the relevant NMR

parameter, named NMR ratio. The NMR ratio reported as a function of the reference fat

content exhibits a linear dependence fit by a linear regression with a correlation coefficient

of 0.996 indicating that a reasonable determination of the fat content is possible.

The relaxation method was used for calibrating the fat content in packaged

mayonnaises and margarines. In fact, the relaxation time differences of fat and water

protons may be exploited to generate a contrast allowing the relative content determination.

Again, a linear correlation was found between the fat percentage measured by NMR and

the reference fat content. In this case data were analyzed by a chemometric approach

obtaining a correlation coefficient of 0.991. The linear correlation found with both methods

between the processed NMR signal and the fat content obtained by reference methods may

allow the use of single-sided NMR for fat measurements even in packaged food. The

chemometric approach was applied to the NMR signal which can be analyzed using

supervised or unsupervised pattern recognition methods (Vandenginste et al., 1998). These

methods applied to food analysis may be qualitative or quantitative. The knowledge of

reference values allows for the calibration of the NMR response (Guthausen, 2016).

In another paper Martini et al. compared the determination of solid fat content (SFC)

obtained using conventional off-line NMR instrument with determination obtained by

single-sided NMR sensor (Martini et al., 2005). Authors explored the use of single sided

NMR to measure variation of SFC on-line during the crystallization of a fat product.

Interesterified hydrogenated palm oil was added to canola oil in different proportions to

obtain blends with different SFC values. Two different experiments were carried out to

study whether the motion during the crystallization affected the measurements. In one

experiment, agitation was stopped during the measurement and then restarted to let the

crystallization continue until the next measurement. In the other experiment measurements

were carried out under agitation. To eliminate the temperature effect on measurements

carried out by single-sided sensor a proper correction was applied to the collected data. In

the former case the corrected data were close to those collected by a conventional off-line

NMR instrument particularly at intermediate SFC values, whereas in the latter case data

were found to be significantly different from those determined by the off-line instrument.



Haiduc et al. reported a feasibility study on the use of single-sided NMR for the

assessment of the microstructural quality of food material (Haiduc et al., 2007). Authors

investigated model systems consisting of oil-in-water emulsion gels stabilized by proteins.

These systems form complex structures made of fat droplets and a protein aggregate

network where water is dispersed in pores with different size. An important quality

parameter of such systems is the water exudation (WE). In a classical approach WE is

determined by measuring the amount of water lost from the system when subject to

gravitational forces. To establish a relation between signals obtained by single-sided NMR

and WE multivariate calibration techniques were applied. Specifically, to obtain a

calibration model allowing for a physical interpretation, an approach based on multilinear

regression (MLR) was applied to decays collected with a benchtop instrument and with a

single-sided sensor as well. Decays were transformed from the continuous domain to the

discrete domain of T2 distributions and amplitudes using the Nonnegative Least Squares

(NNLS) algorithm. The decays were averaged and the obtained data fitted without any

initial guess on the number of components or T2 values. To summarize, the MLR model

was built using the NNLS amplitudes as responses, and the functional parameters as

predictors. The quality of MLR model applied to both set of data is comparable indicating

that single-sided NMR may also allow for the assessment of WE in a through package

mode.

Single side NMR was also employed in quality control of sealed liquid foods. Stork et

al. applied single-sided and semisingle-sided NMR sensors with a reduced magnetic field

gradient for determining the oxygen content in unopened bottles with superoxygenated

table water, and compared the results obtained with results obtained by conventional NMR

(Stork et al., 2006). A good comparison between the spin-lattice relaxation rate measured

with a single-sided NMR sensor and that measured by conventional NMR was found. The

semisingle-sided sensor was also used for determining the oxygen concentration in a

commercial bottle before and after opening the bottle by measuring the water relaxation

time. The sensor was able of monitoring the oxygen concentration that was constant before

opening, and progressively decreased after opening the bottle, indicating the possible use

of this sensor for on-line application after a suitable calibration procedure.

Another paper reports a method based on the use the profile NMR MOUSE to detect

adulteration of virgin olive oil through sealed bottles (Xu et al., 2014). With this method

the transverse relaxation time and the self-diffusion coefficient of virgin olive oils

adulterated with different percentages of sunflower oil and red palm oil, were investigated.

In this paper authors developed a 2-dimensional Laplace inversion according to the

algorithm by Venkataramanan to reconstruct the 2-dimensional probability density

distribution of the transverse relaxation time and self-diffusion coefficient

(Venkataramanan et al., 2002). Figure 4 shows the D-T2 distribution map of different olive

oils which are well separated in the self-diffusion coefficient (D) direction whereas overlap

in the transverse relaxation time (T2) direction.



Figure 4. D-T2 distribution maps of oils. From the top to the bottom a) pure extra virgin olive oil, extra

virgin olive oil adulterated with 10 and 20% of sunflower oil, pure sunflower oil. b) Pure red palm oil,

extra virgin olive oil adulterated with 10 and 20% of red palm oil, and pure extra virgin olive oil

(reproduced with permission from Xu et al., 2014).

From top to bottom peaks correspond to pure extra-virgin olive oil, extra virgin olive

oil adulterated with 10 and 20% of sunflower oil, and pure sunflower oil. Figure 4b shows

the D-T2 distribution of red palm and extra virgin olive oil mixtures. From the top to the

bottom peaks correspond to pure red palm oil, extra virgin olive oil adulterated with 10 and

20% of red palm oil, and extra virgin olive oil. Oils are well separated in D direction and,

at a lesser extent, in T2 direction. These data indicate that the adulteration of extra virgin

olive oils with sunflower oil may be readily detected from the self-diffusion coefficient

behavior, whereas the adulteration with red palm oil can be detected from both diffusion

and transverse relaxation behavior.

The tomato paste processing industry is very interested in developing methods to detect

tomato spoilage in 1,000 L non-ferrous, metal-lined containers without violating the seal.

Early spoilage detection would eliminate shipping costs and disposal costs when spoiled

tomato paste arrives at destination. Pinter et al. explored the relaxation properties of sterile

and unsterile tomato paste (Pinter et al., 2014). Authors found that spoilage in tomato paste

test samples leads to longer longitudinal relaxation times using a conventional benchtop

NMR system. Specifically, the steady state spin lattice relaxation time obtained by the

regression of T1 values measured as a function of time, was the parameter chosen to

differentiate between spoiled and unspoiled tomato paste. This result prompted them to

extend the study to 1,000 L non-ferrous, metal-lined totes using single-sided NMR. In order

to perform measurements directly on the metal container its effect on the circuit tuning and

impedance matching properties was properly compensated. A modified saturation recovery

sequence was used to measure the longitudinal relaxation with single-sided NMR. The

NMR signal and T1 values obtained from the large format container with the single-sided

sensor suggested that this device can be used to study tomato paste spoilage in factory

process environments.

Unilateral NMR sensor was also able to asses in vivo the fat content in fish,

demonstrating the feasibility for online quality control. Veliyulin et al. developed and

1T



tested a new method for the rapid measurement of the fat content in live (or slaughtered)

Atlantic salmon, based on a mobile low-field NMR analyzer, has been developed and tested

(Veliyulin et al., 2005). The instrument, calibrated against a set of reference samples (fish

oil in agarose), was used for non-destructive fat determination of the Norwegian quality

cut of anaesthetized fish. The distribution of transverse relaxation times when measured

with a conventional benchtop NMR instrument shows three peaks. The shortest component

is usually ascribed to water closely associated with macromolecules, the intermediate one

to intracellular water or water within the myofibrillar structure, whereas the longest one

accounts for both lipids and water. The interference of the fat signal with the signal from

extra- myofibrillar water both contributing to the longest component makes a direct

quantification of the fat or water content from T2 measurements very difficult. When

measurements are performed by a single-sided NMR sensor, the strong field gradient

generated by the sensor makes the self-diffusion coefficient contribute strongly to the

effective transverse relaxation time. Because the average self-diffusion coefficient of water

in fish muscle is considerably faster than that of fat, this difference may be exploited to

make accurate quantification of the fat component from the transverse relaxation response.

Figure 5 compares the transverse relaxation curves of pure fish oil and Atlantic salmon

white muscle obtained applying a CPMG sequence optimized to ensure the best separation

between water and fat components. A significant correlation was found between the fat

content measured by single-sided NMR and that measured by chemical extraction data

obtained after slaughtering the same fish. Furthermore, the mobile single-sided sensor was

used to map the fat distribution over the whole fish surface. The rapid fat determination

with easy calibration routines showed that single side NMR has potential for

implementation in connection with on-line quality control for in vivo assessment of fat

content in salmon.

Figure 5. Transverse relaxation curves of pure fish oil and Atlantic salmon white muscle measured at 4°C

using a single-sided NMR sensor (reproduced with permission from Veliyulin, 2005).



Relaxometry using a single side sensor was employed to investigate the water status

and ripening of fruits. Capitani monitored the water status of kiwifruits as a function of

season using a single-sided NMR sensor (Capitani et al., 2010; Capitani et al., 2013). Using

this sensor it was possible to measure the entire fruit at a depth of about 0.5 cm from the

peel surface without cutting it (Figure 6a). The T2 distribution of a mature kiwifruit

measured by single-sided NMR shows three peaks (Figure 6b). According to the literature

the longest T2 component was assigned to protons in vacuole, the intermediate one to

proton in cytoplasm and extra-cellular space, and the shortest one to proton in cell walls. It

must be noted that the presence of the strong magnetic field gradient of the single-sided

sensor, heavily shortens T2 values. In fact, in the case of ripened kiwifruit measured in

homogeneous fields literature data report average T2 values of 800-1000 ms for the longest

component, 200-400 ms for the intermediate component, and 20-80 ms for the shortest one.

With single-sided NMR the longest component is as short as 20 ms. Nevertheless,

single-sided NMR was suitable to monitor the growth of kiwifruit. Because the shortest

component was very poorly affected by the season, only the intermediate and longest

components were taken into account. Figure 7 reports the average values of the

intermediate component ( ) and the longest component ( ) measured on nine

kiwifruits of three cultivar, namely Hayward (a, b), CI.GI. (c, d), and Zespri (e, f) at

different stages of development. In all cultivar T2 values were found to be rather constant

until October, thereafter they increased. The tendency toward longer T2 relaxation times

later in the season is consistent with a change in the fruit texture occurring during fruit

development. However, whereas in Hayward and CI.GI. a gradual lengthening of T2 was

observed, in Zespri, a net and sharp lengthening of both components occurred between

October and November, indicating the earlier maturation of Zespri.

Figure 6. a) Measurement of intact kiwifruit with a single-sided NMR sensor. b) Transverse relaxation

times distribution measured on a ripened kiwifruit.

2aT 2bT



Figure 7. Average T2 values, namely and measured on nine kiwifruits versus the developmental

stage of Hayward (a, b), CI.GI. (c, d), and Zespri (g, h) kiwifruits (Reproduced with permission from

Capitani et al. 2013).

The ripening of kiwifruits was also monitored in field on fruits attached to the plant

during three campaigns of measurement carried out in October, November and December.

Figure 8 shows the transverse relaxation decays measured in field. In all cultivar a

lengthening of the decays with the season was observed, however in Zespri (Figure 8c) the

process was complete already in November, in fact decays measured in November and

December perfectly overlap. In agreement with data collected in laboratory on intact

kiwifruits, these data confirmed the earlier maturation of Zespri.

2aT 2bT



Figure 8. Transverse relaxation curves measured in field on Hayward (a), CI.GI (b), and Zespri (c) with

the corresponding relaxation times distributions.

The profile NMR MOUSE was used to get preliminary results on three fresh intact

blueberries and the same blueberries let to wither outside the fridge for three and six days

(Capitani et al., 2014) (Figure 9).

With this sensor NMR depth profiles were collected, these profiles encode the

amplitude of the 1H NMR signal as a function of the depth scanned. The amplitude of

profiles measured on withered blueberries was found to be lower than that measured on

fresh blueberries, indicating a loss of water that was quantified by integrating the profiles.

After three days of withering a loss of water of 13% (a), 11% (b), and 16% (c) was

measured, whereas after six days the loss of water was found to be 30% (a), 20% (b), and

34% (c). Therefore, the integral of the profile is a suitable index of water loss. The same

index may be used to monitor changes in foodstuff texture due to maturation, ripening,

water and osmotic stress, for monitoring the effect of different types of processing on food

matrices, and the effect of storage.



Figure 9. Depth profiles of three fresh blueberries (a, b, and c), and profiles of the same blueberries let to

wither for three and six days (Reproduced with permission from Capitani et al., 2014).

Adiletta et al. used the profile NMR MOUSE to investigate the drying process on pears

(Adiletta et al., 2015). Drying is an important process for conservation and marketing of

fruits due to their high water activity which makes them perishable. Therefore, the

knowledge and optimization of drying process are very important to minimize thermal

damage and quality loss. Information on drying kinetics was obtained by measuring the

intensity of 1H NMR signal as a function of the thickness of the sample. Figure 10a shows

the comparison among profiles of fresh pear and pears dried for 3, 6, 15, 20, 29, and 48

hours. The amplitude of profiles measured in dried samples progressively lowered and also

the thickness of the profiles progressively reduced indicating a loss of water with increasing

the drying time and the consequent shrinkage. Figure 10b shows the good agreement

between values (Mt/M0), obtained by gravimetric method and values (It/I0) obtained by

integrating NMR depth profiles, with a regression coefficient of 0.978. An evaluation of

the loss of water in the outer, intermediate, and central regions of pears samples was

obtained simply by integrating the profiles in three corresponding regions (without cutting

the samples); the results obtained are reported in Figure 10c. As expected the water content

of the outer region decreases more quickly than that of the intermediate and central ones,

such differences are much reduced at long drying times.

Figure 10. a) NMR depth profiles of fresh pear and pear samples dried at 50 °C for 3, 6, 15, 20, 29, and

48 h. b) Relationship between the water loss measured by gravimetric method (Mt/M0) and single-sided

NMR (It/I0). c) Water loss in exterior, intermediate, and central part of pear samples measured by single-

sided NMR. (Reproduced with permission from Adiletta et al., 2015).



3. MAGNETIC RESONANCE IMAGING APPLICATIONS

We hereby reported a short survey of the most relevant works related to the application

of Magnetic Resonance Imaging (MRI) in food chemistry. De facto, a relatively large body

of literature has highlighted the determinant role played by MRI, through different MRI

experiments and applications, as a rapid, quantitative, and non-invasive NMR technique to

evaluate the quality of fresh, stored and processed food products. Although the image

resolution is restricted to few micrometers and the costs of MRI analysis are still rather

expensive, this technique offers the unique advantage to investigate the inner morphology

of food products, thereby progressively enlarging our knowledge on the number of factors

that affect their quality, authenticity, shelf-life, perishability and safety for human health.

3.1. Internal Morphological Structures of Food Products

Numerous works have so far demonstrated how MRI high-resolution images may serve

as a reliable tool to appreciate critical morphological changes in edible fruits and

vegetables. Sequi et al. demonstrated that MRI spectroscopy is capable to differentiate

cherry tomatoes grown in Protected Geographical Indication (PGI) area from those grown

in non-PGI area on the basis of morphological and physical parameters (Sequi et al., 2007).

In particular, the proposed approach consisted of a set of four empirical equations taking

into account the pericarp thickness, the width of the inner and outer spherical crowns

composing the pericarp itself and their transverse relaxation times T2. Later, the same

authors revealed changes in both morphological structure and relaxation times of PGI

Pachino cherry tomatoes depending on seasonal growing conditions (Ciampa et al., 2010).

Remarkably, T1-weighted images revealed a white film covering the placental cavities

exclusively for tomatoes harvested in winter and spring seasons (Figure 11). Moreover, the

T1 values associated to the site interested by the film resulted to be relatively low thus

indicating intense interactions with the cellular tissues (Ciampa et al., 2010).

Figure 11. T1-weighted image of Pachino cherry tomatoes (Lycopersicon esculentum cv. Shiren)

harvested during the winter (A) and summer (B) seasons. Reprinted from Journal of Food Chemistry with

permission by Elsevier (Ciampa et al., 2010).



MRI also allowed to quantify the microstructure of apple cortex that is involved in

mechanical and transport properties of fruit tissues (Musse et al., 2010). In order to confirm

the reliability of these results, the authors acquired images on two magnets at different

strength and performed a comparison with X-ray microtomography technique. Spin echo

MRI images enabled to asses the variation in water distribution in pea seeds, harvested at

different seed stages, and indicated a gradual dislocation of water from the inner to the

outer part of cotyledons, likely orientated in the same direction as starch accumulation

(Garnczarska et al., 2008). Thybo et al. demonstrated that, even though MRI was able to

efficiently differentiate several potato varieties, the histogram image analysis did not show

a robust correlation with dry matter content of tubers (Thybo et al., 2003).

The MRI technique may also single out important details concerning the vascular

system of plants. A valid example is represented by a work of Salerno et al. in which the

reported clear images of internal morphology of radishes showed the radial distribution of

xylematic and phloematic vessels (Salerno et al., 2005). Interestingly, Marconi et al. have

observed several changes occurring in the internal structure of radish tuber grown in two

different types of soils (sandy and clay-loam) and irrigated with water contaminated by

different concentrations of Arsenic (V) (Marconi et al., 2010). It was observed that the As-

uptake induced the formation of large cavities, with the detachment of the external cortex,

and that the effect was even enhanced in case of radishes grown in clay soils (Marconi et

al., 2010). The importance of this issue relies on the fact that the uptake of As (V) in edible

vegetables, even in small amounts, constitutes a primary risk for food safety and human

health.

The use of phytoregulators to increase the yields in agriculture represents another issue

of food safety concern. In fact, since these agrochemicals are prohibited in both biological

and integrated agriculture, it is necessary to apply analytical tools capable to identify and

discourage their possible use. MRI has been applied to trace the phytoregulators addition

in kiwi fruit cultivation. Since hormones-related metabolic residues are no longer present

at harvest time in fruits characterized by long maturation period, such as kiwi, their

identification must be obtained during the growth stage. This was achieved on hormone-

treated kiwis by examining the internal morphology and, in particular, the qualitative

changes occurring in the epicarp and mesocarp (Valentini et al., 2009).

The MR structural images have been also used for non-invasive investigations on other

common problems of agrofood products, such as the malformations growth, the effects of

parasites, the early detection of mechanical damages and physiological diseases. For

example, the watercore is a physiological disorder in apples which occurs mostly late in

the season in over-mature fruits when they are still pending on the tree. Watercore-affected

apples are not easily recognized because only their internal tissues are interested while the

outer part of the fruit appears intact. Wang et al. and Clark et al. have applied MRI to study

the watercore occurrence in Red Delicious and Fuji apples apple varieties, respectively

(Wang et al., 1988; Clark et al., 1998b). Both works showed that the disorder implies a



water accumulation in the intercellular spaces which is organized in either a block or a

radial form. Zion et al. developed an algorithm capable to quantify the bruised region in

apples by spin echo images (Zion et al., 1995). MRI has been also used to detect and

progressively quantify internal defects such as the browning in Fuji apples exposed to

either high or low CO2 concentrations (Gonzales et al., 2001). Recently, the correlation

between the solar radiation and the development of radial watercore in apples was shown

by using MRI (Melado et al., 2012). The mealiness is a further negative attribute of sensory

texture which combines the sensation of a desegregated tissue with that of juiciness lack.

Barreiro et al. have applied MRI to identify the mealiness through T2 maps which, in both

mealy apples and wooly peaches, were characterized by relatively short T2 values (Barreiro

et al., 2000). Recently, it has been shown that when MRI is applied on a relatively high-

field magnet (11.7 Tesla), it may provide high resolution images capable to unravel several

quality parameters in apples cortex and, in particular, the internal browning and the

influence exerted by the storage time (Defraeye et al., 2013). MRI has been also used to

monitor the increase of oil content in both mesocarp and kernel of oil palm fruits up to 21

weeks after anthesis (Shaarani et al., 2010).

Concerning the detection of a fungal attack, an interesting example was reported by

Maas et al. in which proton distribution images, T1-weighted images and T1-related

histograms of strawberries affected by B. cinerea, C. acutatum and P. cactorum were

examined. Infected areas were visibly distinguishable from healthy areas as well as

relaxation times and water proton densities resulted shorter and greater, respectively, in the

former ones (Maas & Line, 1995).

While most of literature on MRI for the evaluation of internal structure and texture of

food products has been focused on edible fruits and vegetables, there are also several and

noteworthy works in which different categories of food have been considered. For

example, Cernadas et al. demonstrated that the combination of MRI images with a

statistical texture analysis may allow both the classification of Iberian pork loins and the

prediction of several sensorial characteristics (Cernadas et al., 2005). The combination of

MRI techniques with image analysis methods may predict, in agreement with sensory

panel, the texture of several kinds of soft cheese (Mariette & Gollewet, 2001). Likewise,

MRI technique have been also used to study various food products deriving from cereals,

such as wheat-derived spaghetti (Sekiyama et al., 2012), soy and wheat dough and kernels

(Simmons & Vodovotz, 2012, Castro et al., 2010) and dough pastry (Manzocco et al.,

2012).

3.2. Food Quality as Revealed by MRI

So far, a discrete body of literature has proved that MRI not only represents an

excellent technique to supply a detailed (up to few tens of micrometers) visualization of



internal structure of intact food products, but can also provide indisputable elements to

both assess the quality of food and reveal the influence exerted by important factors such

as the storage procedures, the food industrial processing or the temperature treatments. In

this context, the most relevant applications of MRI will be cited in the following paragraphs

and grouped as a function of applied procedure or treatment.

3.2.1. Food Storage

Several works have reported MRI applications for the assessment and comparison of

different storage conditions. For example, Taglienti et al. have observed significant and

time-dependent changes in the mobility and distribution of water in kiwifruits, during

postharvest period, depending on the temperature and relative humidity adopted for the

storage (Taglienti et al., 2009). In particular, they observed that the organization and

mobility of water assumes a determinant role during storage and the minimal variation in

vapor pressure, due to changes in water loss rate and temperature, altered this organization,

thus inducing textural changes and kiwifruit softening. Moreover, it has been shown that a

correlation exists between texture parameters of several varieties of apples and the effects

due to the length of storage period (Letal et al., 2003). The structural changes resulting

from dehydration induced in radish by storage at a low relative humidity were also object

of MRI studies (Salerno et al., 2005). Wang (Wang & Brennan, 1995) have observed that

relatively high temperatures achieved during the storage of fruits may enhance the

movement of water from superficial layers, thus forming lens-shaped cavities, typical of

CO2-induced injuries which are responsible for the browning of the tissue (Elgar et al.,

1998).

The fruit defect represented by internal browning usually occurs during controlled

atmosphere storage. Generally, in case of browning, MRI has identified three areas of

tissue: normal, slightly dark and very dark. The slightly darker area is predominant when

the conservation takes place at low temperature and low CO2 concentration (0 °C and 3%,

respectively), and it is possible to distinguish it from normal tissue through MRI because

of lower signal intensity and shorter transverse relaxation times. Conversely, the very dark

tissue is formed at high temperatures and CO2 concentration (20 °C and 18% respectively)

and are characterized by a very high signal intensity and longer T2 (Clark et al., 1998b).

Osmotic dehydration represents an effective method to preserve fruits and vegetables.

MRI was used to examine the osmotic dehydration of broccoli mediated by trehalose, as

osmotic solution, and permitted to follow the variation in their glass transition on the basis

of water state (Xin et al., 2013). De Rossi et al. used diffusion-based MRI experiments to

confirm the validity of Fickian-based unsteady state diffusion model in describing the

kinetics of osmotic dehydration for apple tissues in a sucrose solution (De Rossi et al.,

2008). These authors also displayed the existence of a dehydration front moving from the

edge to the core of apples during the process.



The application of uncontrolled and undesired physical pressures may occur during

transport and storage of fruit and vegetables with negative influence on the quality. Otero

(Otero & Prestamo, 2009) investigated the effects exerted by the application of controlled

pressures on strawberries and evaluated the extent of physical tissue damage on the basis

of T1-, T2- and diffusion-weighted MRI images.

Several works have used MRI also to study processes occurring during food packaging

and the effectiveness of treatments aimed to preserve food quality. For example, MRI

permitted to assess the capacity of a chitosan-based coating product to slow down the

maturity and decay in two varieties of citrus fruits and, at the same time, their preservation

by inhibiting the fungal growth (Galed et al., 2004). The authors showed that the fungal

formation may be revealed by T2-weighted images as an accumulation of mobile water in

the floral meristem. This is accompanied by a dehydrated area in the epicarp that

corresponds to the tissue portion where the fungi are growing (Galed et al., 2004). MRI

also allowed to monitor the process of moisture redistribution occurring in commercial

sandwiches containing sausages or ham and stored in sealed plastic packages under N2

atmosphere (Ramos-Cabrer et al., 2006). Interestingly, these authors have successfully

used a variant of the Single Point Imaging (SPI) pulse sequence (Kennedy et al., 1998)

with the aim to circumvent the limitation of extremely restricted mobility of water fraction

in sandwiches.

3.3. Freezing-Thawing

A valid strategy to preserve, as much as possible, the quality of food products consists

in the application of freezing, followed by a low-temperature storage (generally around -

20°C, for a maximum period that depends on food properties), and finally by thawing prior

to product consumption. However, extreme or inappropriate freeze-thawing conditions

may significantly influence texture, firmness, water distribution and organoleptic

properties of food and the MRI technique appears especially suitable to investigate these

parameters. In fact, it has been proved that T2–weighting is one of the best MRI tools to

identify freezing injuries in food, as shown by several studies which examined frozen-

thawed products such as courgettes (Duce et al., 1992), blueberries (Gamble, 1994),

kiwifruits (Kerr et al., 1997) and oranges (Hernandez-Sanches et al., 2004). The T2

increase, which commonly results from the freezing procedure, may be ascribed to both

the cell lysis due to ice crystals formation as well as to protein denaturation that affects the

overall food structure (Erikson et al., 2012). Moreover, differently from T1 values, the T2

values are very sensitive in identifying freeze-damaged cucumbers by showing higher

water mobility localized in damaged tissues (Kotwaliwale et al., 2012). An interesting

study reported by Koizumi et al. showed that spin-echo-based MRI images of a specific

MRI system, enabled to follow the changes during the thawing process of frozen vegetables



such as green soybeans, broad beans, okra, asparagus and taro (Koizumi et al., 2006).

Moreover, T1 values and magnetization transfer rate of water were shown to differentiate

fresh meat samples belonging to several species (lamb, beef and pork) from those deriving

from a freezing–thawing process (Evans et al., 1998). The application of MRI was explored

by Nott (Nott et al., 1999) as a potential tool for the authentication of fresh rainbow trout

and its distinction from the same fish after a freezing–thawing process, while Foucat

(Foucat et al., 2001) monitored the rainbow trout quality by MRI as a function of the frozen

storage period. Measurements by MRI of water content and distribution, as well as

relaxation times, contributed to confirm that ultrasonic treatment is a promising way to

decrease the freezing time of frozen radish samples and better preserve their quality (Xu et

al., 2015). Differences in MRI diffusion coefficients in mozzarella cheeses ("pasta-filata"

and "pasta non-filata") were significantly related to the days elapsed between product

manufacture and freezing, to the length of frozen storage and to the temper period at 5 °C

(Kuo et al., 2003).

Another strategy to store and preserve food products such as fish flesh and meat

consists in the salting procedure. In this context, a noteworthy example is represented by

the work of Aursand (Aursand et al., 2010), in which, through an SPI MRI pulse sequence,

the effects of the salting of Atlantic salmon have been investigated by means of 23Na MRI.

The authors demonstrated that the combination of 1H and 23Na MR images enabled the

quantification of the uptaken sodium and revealed the tight correlation between salmon fat

distribution and salt diffusion/distribution. Recently, MRI has been combined with data

mining to study salt-diffusion during the post-salting stage of Iberian hams and to predict

salt content (Caballero et al., 2016).

3.4. Temperature Treatment

Temperature plays a determinant role not only for food preservation, but also in many

processing techniques aiming to improve products quality and safety. Temperature

treatments may be in fact involved in both phases of production and food transformation.

Since these treatments are also applied to dairy products, several interesting MRI studies

have been developed to investigate their effects (Anedda, 2015; Mulas et al., 2013). In

particular, Mulas described a new protocol to discriminate Sardinian sheep milk cheese

originated from either heat-treated or raw milk. The proposed MRI procedure enabled

samples differentiation through proton T2 distribution. A significantly larger abundance of

water protons population strongly interacting with proteins accompanied by a smaller long

T2 population, has been detected in heat-treated milk cheeses as compared with raw milk

counterparts (Mulas et al., 2013). The MRI technique was also employed to examine cereal

products subjected to different thermal processes, such as steeping, at low and high

temperatures, and drying processes. For example, Ruan (Ruan et al., 1992) studied the



pericarp quality of corn kernels as a function of water absorption upon temperature steeping

and drying processes. The authors stated that changes in MRI signal intensity were due to

a lower proton density resulting from a moisture loss. Interestingly, their results proved

how MRI may potentially be exploited for process and quality control, by monitoring

moisture transfer and related structural changes during drying processes.

The cooking process represents a further food transformation of primary importance

when one wants to control sensory, nutritional and technological qualities of cooking end-

products. Bouhrara (Bouhrara et al., 2011) acquired MR images of meat samples during

cooking and revealed that the structural changes occurring in meat were mostly caused by

modifications of both cell and connective-tissue proteins. It has been also demonstrated

that both T1 and T2 values progressively decreased with increased heating time in cooked

meat (Shaarani et al., 2006). They showed a loss of water associated with a decreased

rotational mobility, and spatial differences between the core and outer layers of samples.

The T1 and T2 relaxation times are expected to decrease when the denaturation of structural

proteins begins (myosin), thus inducing the release of water from cells (myofibrils) into

the extracellular space. The expulsion of water is a consequence of the contraction of

connective tissue which expels water first into the inter bundle space and then out of meat

(Bouhrara et al., 2011). Remarkably, MRI helped in identifying the heating pattern

developed in a sauce that contained meat pieces and was processed by either microwave or

conduction-limited conventional heating (Bows et al., 2001). In this work, MRI enabled

the mapping of the spatial distribution of temperature and demonstrated that a different

heating pattern occurred as a function of the heating procedure (Bows et al., 2001).

CONCLUSION

In this section, we have presented an overview regarding the applications of low-field

NMR relaxometry and NMR-imaging to food products. Despite the considerable progress

in this field, several important challenges remain.

Developments in electronics and magnet technology should allow the development of

new dedicated pulses and NMR sequences which, together with gradient assisted

spectroscopy could open up a large field for improving performance and consequently for

the application of low-field NMR instrumentation. Further improvement of current

instrumental benchtop NMR equipment will enable the application of more rapid and

advanced single-shot measurements. Such techniques will be of particular interest for

online and/or real-time applications. An example is a recent online approach based on a

continuous wave free precession (CWFP) technique, which has been demonstrated in

feasibility studies on seeds and meat as well as other food applications that can also be

envisaged. The current arsenal of 2D sequences will expand with experiments that can

reveal specific structural features. An example is the incorporation of field cycling into 2D



correlation experiments. Major developments can be expected in strategies for assigning

signals in the resulting 2D correlation plots. Processing the acquired TD data sets into

meaningful diffusometric/relaxometric correlation plots will remain a challenge.

Potentially, more detailed images may be revealed in MRI by combining advanced

filtered pulse sequences with the inoculation of selected paramagnetic species interacting

with specific food compartments. In addition, a remarkable potential resides in the recent

development of Rheo-MRI enabling the determination of parameters, such as flow and

rheological properties, which can be related to product quality, freshness and shelf life.

However, the fact that both MRI instruments and software are mostly research oriented

still prevents the design of standard operating procedures for food applications, thus

hampering the extension of the MRI technique to routine analyses concerning industrial

processes and quality control.

ACKNOWLEDGMENTS

This work has been carried out within the Italian project of Regione Lazio Lr 13/2008

entitled “e-ALIERB: un OPEN LAB per caratterizzare e valorizzare i prodotti alimentari

ed erboristici del territorio laziale.”

REFERENCES

Adiletta, G., Russo, P., Proietti, N., Capitani, D., Mannina, L., Crescitelli, A., & Di Matteo,

M. (2015). Characterization of pears during drying by conventional technique and

portable non invasive NMR. Chemical Engineering Transactions, 44, 151-156.

Anedda, R. (2015). Magnetic Resonance analysis of dairy processing: suitable tools for the

dairy industry. In F. Capozzi, L. Laghi, & P. S. Belton (Eds.), Magnetic Resonance in

Food Science: Defining Food by Magnetic Resonance. (First edition pp. 51-64).

Cambridge, UK: Royal Society of Chemistry.

Aursand, I. G., Erikson, U., & Veliyulin, E. (2010). Water properties and salt uptake in

Atlantic salmon fillets as affected by ante-mortem stress, rigor mortis, and brine

salting: A low-field 1H NMR and 1H/23Na MRI study. Food Chemistry, 120, 482-489.

Barreiro, P., Ruiz-Cabello, J., Fernandez-Valle, M. E., Ortiz, C., & Ruiz-Altisent, M.

(1999). Mealiness assessment in apples using MRI techniques. Magnetic Resonance

Imaging, 17, 275-281.

Barreiro, P., Ortiz, C., Ruiz-Altisent, M., Ruiz-Cabello, J., Fernandez-Valle M. E.,

Recasens, I., Asensio M. (2000). Mealiness assessment in apples and peaches using

MRI techniques. Magnetic Resonance Imaging, 18(9), 1175-1181.



Barreiro, P., Moya, A., Correa, E., Ruiz-Altisent, M., Fernandez-Valle, M., Peirs, A.,

Wright K. M., & Hills, B. P. (2002). Prospects for the rapid detection of mealiness in

apples by nondestructive NMR relaxometry. Applied Magnetic Resonance, 22, 387-

400.

Belton, P. S. (1984). Nuclear magnetic resonance and photoacoustic spectroscopy. In H.

W.-S. Chan, (Ed.), Biophysical Methods in Food Research (pp. 103-37). London, UK:

Blackwell Scientific.

Blümich, B., Perlo, J., & Casanova, F. (2008). Mobile Single-side NMR. Progress in

Nuclear Magnetic Resonance Spectroscopy, 52, 197-269.

Bordini, A., Picone, G., Babini, E., Vignali, M., Danesi, F., Valli, V., Di Nunzio, M., Laghi,

L., & Capozzi F. (2011). NMR comparison of in vitro digestion of Parmigiano

Reggiano cheese aged 15 and 30 months. Magnetic Resonance in Chemistry, 49, S61-

S70.

Borgia, G. C., Brown, R. J. S., & Fantazzini, P. (1998). Uniform-penalty inversion of

multiexponential decay data. Journal of Magnetic Resonance, 132(1), 65-77.

Bouhrara, M., Clerjon, S., Damez J. L., Chevarin, C., Portanguen, S., Kondjoyan, A., &

Bonny, J. M. (2011). Dynamic MRI and thermal simulation to interpret deformation

and water transfer in meat during heating. Journal of Agricultural and food Chemistry,

59, 1229-1235.

Bowen, J. H., & Watkins, C. B. (1997). Fruit maturity, carbohydrate and mineral

contentrelationships with watercore in ‘Fuji’ apples. Postharvest Biology and

Technology, 11, 31-38.

Bows, J. R., Patrck, M. L., Nott, K. P., & Hall, L. D. (2001). Three-dimensional MRI

mapping of minimum temperature achieved in microwave and conventional food

processing. International Journal of Food Science & Technology, 36, 243-252.

Brosio, E., Belotti, M., & Gianferri, R. (2008). Diffusive and chemical exchange model:

practical hits to investigate water dynamics in foods. In L. V. Greco, & M. N. Bruno

(Eds.), Food Science and Technology (First Edition pp. 323-340). Hauppauge (NY):

Nova Science Publishers.

Brosio, E., & Gianferri, R. (2011). NMR spectroscopy in dairy products characterization.

In R. D. Foster (Ed.), Cheese: Types, Nutrition and Consumption (First edition pp. 183-

206). Hauppauge (NY): Nova Science Publishers.

Caballero, D., Caro, A., Rodríguez, P. G., Duran, M. L., Del Mar Avila, D., Palacios, R.,

Antequera, T., & Perez-Palacios, T. (2016). Modeling salt diffusion in Iberian ham by

applying MRI and data Mining. Journal of Food Engineering, 189, 115-122.

Callaghan, P. T., Jolley, K. W., & Lelievr, J. (1979). Diffusion of water in the endosperm

tissue of wheat grains as studied by pulsed field gradient nuclear magnetic resonance.

Biophysical Journal, 28, 133-141.

Capitani, D., Mannina, L., Proietti, N., Sobolev, A. P., Tomassini, A., Miccheli, A., Di

Cocco, M. E., Capuani, G., De Salvador, R., & Delfini, M. (2010). Monitoring of



metabolic profiling and water status of Hayward kiwifruits by Nuclear Magnetic

Resonance. Talanta, 82(5), 1826-1838.

Capitani, D., Mannina, L., Proietti, N., Sobolev, A. P., Tomassini, A., Miccheli, A., Di

Cocco, M. E., Capuani, G., De Salvador, F. R., & Delfini, M. (2013). Metabolic

Profiling and Outer Pericarp Water State in Zespri, CI.GI, and Hayward kiwifruits.

Journal of Agricultural and Food Chemistry, 61(8), 1727-1740.

Capitani, D., Sobolev, A. P., Delfini, M., Vista, S., Antiochia, R., Proietti, N., Bubici, S.,

Ferrante, G., Carradori, S., De Salvador, F. R., & Mannina, L. (2014). NMR

methodologies in the analysis of blueberries. Electrophoresis, 35, 1615-1626.

Castell-Palou, A., Rosselló, C., Femenia, A., Bon, J., & Simal, S. (2011). Moisture profiles

in cheese drying determined by TD-NMR: Mathematical modeling of mass transfer.

Journal of Food Engineering, 104, 525-531.

Castro, C., Gazza, L., Ciccoritti, R., Pogna, N., Rossi, C. O., Manetti, C. (2010).

Development of wheat kernels with contrasting endosperm texture characteristics as

determined by magnetic resonance imaging and time domain-nuclear magnetic

resonance. Journal of Cereal Science, 52, 303-309.

Cernadas, E., Carrion, P., Rodriguez, P. G., Muriel, E., & Antequera, T. (2005). Analyzing

magnetic resonance images of Iberian pork loin to predict its sensorial characteristics.

Computer Vision and Image Understanding, 98, 345-361.

Chayaprasert, W., & Stroshine, R. L. (2005). Rapid sensing of internal browning inwhole

apples using a low-cost, low-field proton magnetic resonance sensor. Postharvest

Biology and Technology, 36, 291-301.

Chen, P., McCarthy, M. J., Kauten, S. M. R., Sarig, Y., & Han, S. (1993). Maturity

evaluation of avocados by NMR methods. Journal of Agricultural Engineering

Research, 55, 177-187.

Cho, S. I., Bellon, V., Eads, T. M., Stroshine, R. L., & Krutz, G.W. (1991). Sugar content

measurement in fruit tissue using water peak suppression in high resolution 1H

magnetic resonance. Journal of Food Science, 56, 1091-1094.

Cho, B. K., Chayaprasert, W., & Stroshine, R. L. (2008). Effects of internal browning and

watercore on low field (5.4 MHz) proton magnetic resonance measurements of T2

values of whole apples. Postharvest Biology and Technology, 47, 81-89.

Ciampa, A., Dell’Abate M. T., Masetti, O. Valentini, M., & Sequi, P. (2010). Seasonal

chemical–physical changes of PGI Pachino cherry tomatoes detected by magnetic

resonance imaging (MRI). Food Chemistry, 122(4), 1253-1260.

Clark, C. J., Drummond, L. N., & MacFall, J. S. (1998a). Quantitative NMR imaging of

kiwifruit (Actinidia deliciosa) during growth and ripening. Journal of the Science of

Food and Agriculture, 78, 349-358.

Clark, C. J., MacFall, J. S., & Bieleski, R. L. (1998b). Loss of watercore from ‘Fuji’ apple

observed by magnetic resonance imaging. Scientia Horticulturae, 73, 213-227.



Colnago, L. A., Moraes, T. B., Monaretto, T., & Andrade, F. D. (2015). Rapid

determination of food quality using steady state free precession sequences in TD-MNR

Spectroscopy. In Magnetic Resonance in Food Science (pp. 1-16).

De Angelis Curtis, S., Curini R., Delfini, M., Brosio, E., D’Ascenzo, F., & Bocca, B.

(2000). Amino acid profile in the ripening of Grana Padano cheese: A NMR study.

Food Chemistry, 71, 495-502.

Defraeye, T., Lehmann, V., Gross, D., Holat, C., Herremans, H., Verboven, P., Verlinden,

B. E., & Nicolai, B. M. (2013). Application of MRI for tissue characterisation of

‘Braeburn’ apple. Postharvest Biology and Technology, 75, 96-105.

De Rossi, A., De Pilli, T., Severini, C., & McCarthy, M. J. (2008). Mass transfer during

osmotic dehydration of apples. Journal of Food Engineering, 86, 519-528.

Duce, S. L., Carpenter, T. A., Hall, L. D. (1992). Nuclear Magnetic Resonance Imaging of

fresh and frozen courgettes. Journal of Food Engineering, 16, 165-172.

Duckworth, R. B. (1975). Water Relations in Foods. London, UK: Academic Press.

Elgar, H. J., Burmeister, D. M., & Watkins, C. B. (1998). Storage and handling effects on

a CO2- related internal browning disorder of ‘Braeburn’ apples. Horticultural Science,

33, 719-722.

Engelsen, S. B., Jensen, M. K., Pedersen, H. T., Nørgaard, L., & Munck, L. (2001). NMR-

baking and multivariate prediction of instrumental texture parameters in bread. Journal

of Cereal Science, 33(1), 59-69.

Erikson, U., Standal, I. B., Aursand, I. G., Veliyulin, E., & Aursand, M. (2012). Use of

NMR in fish processing optimization: a review of recent progress. Magnetic

Resonance in Chemistry, 50, 471-480.

Evans, S. D., Nott, K. P., Kshirsagar, A. A., & Hall, L. D. (1998). The effect of freezing

and thawing on the magnetic resonance imaging parameters of water in beef, lamb and

pork meat. International Journal of Food Science & Technology, 33, 317-328.

Foucat, L., Taylor, R. G., Labas, R., & Renou, J. P. (2001). Characterization of frozen fish

by NMR imaging and histology. American laborator, 22, 38-43.

Galed, G., Fernandez-Valle, M. E., Martınez, A., & Heras, A. (2004). Application of MRI

to monitor the process of ripening and decay in citrus treated with chitosan solutions.

Magnetic Resonance Imaging, 22, 127-137.

Gamble, G. R. (1994). Non-invasive determination of freezing effects in blueberry fruit

tissue by magnetic-resonance-imaging. Journal of Food Science, 59, 571-573.

Gao, Y., Xiao, L., Zhang, Y., & Xie, Q. (2016). The generalized Phillips-Twomey method

for NMR relaxation time inversion. Journal of Magnetic Resonance, 271, 1-6.

Garnczarska, M., Zalewski, T., & Wojtyla, L. (2008). A comparative study of water

distribution and dehydrin protein localization in maturing pea seeds. Journal of Plant

Physiology, 165, 1940-1946.

Gianferri, R., Maioli, M., Delfini, M., & Brosio, E. (2007a). A low-resolution and high-

resolution nuclear magnetic resonance integrated approach to investigate the physical



structure and metabolic profile of Mozzarella di Bufala Campana cheese. International

Dairy Journal, 17, 167-176.

Gianferri, R., D’Aiuto, V., Curini, R., Delfini, M., & Brosio, E. (2007b). Proton NMR

transverse relaxation measurements to study water dynamic states and age-related

changes in Mozzarella di Bufala Campana cheese. Food Chemistry, 105, 720-726.

Gil, A.M., Belton, P. S., & Hills, B. P., (1996). Applications of NMR to food science.

Annual Reports on NMR Spectroscopy, 32, 1-49.

Gonzalez, J. J., Valle, R. C., Bobroff, S., Biasi, W. V., Mitcham, E. J., & McCarthy, M. J.

(2001). Detection and monitoring of internal browning development in ‘Fuji’ apples

using MRI. Postharvest Biology and Technology, 22, 179-188.

Guthausen, A., Guthausen, G., Kamlowski, A., Todt, H., Burk, W., & Schmalbein, D.

(2004). Measurement of fat content of food with single-sided NMR. Journal of the

American Oil Chemists' Society, 81, 727-731.

Guthausen, G., Todt, H., Burk, W., Schmalbein, D., Guthausen, A., & Kamloski A. (2006).

Single sided NMR in food. In G. A. Webb (Ed.), Modern Magnetic Resonance (First

edition pp.1895-1897). Dordrecht, The Netherlands: Springer.

Guthausen, G. (2016). Analysis of food and emulsions. Trends in Analytical Chemistry,

83, 103-106.

Haiduc, A. M., Trezza, E. E., van Dusschoten, D., Reszka, A. A., & van Duynhoven, J. P.

M. (2007). Non-invasive through-package assessment of the microstructural quality of

a model food emulsion by the NMR MOUSE. Food Science and Technology, 40, 737-

743.

Hernández-Sánchez, N., Barreiro, P., Ruiz-Altisent, M., Ruiz-Cabello, J., & Fernández-

Valle, M. (2004). Detection of freeze injury in oranges by magnetic resonance imaging

of moving samples. Applied Magnetic Resonance, 26, 431-445.

Hernandez-Sanchez, N., Hills, B. P., Barreiro P., & Marigheto, N. (2007). An NMR study

on internal browning in pears. Postharvest Biology and Technology, 44, 260-270.

Hills, B. P., & Belton, P. S., (1989). NMR studies of membrane transport. Annual Reports

on NMR Spectroscopy, 21, 99-159.

Hills, B. P., Takacs, S. F., & Belton, P. S. (1990). A new interpretation of proton NMR

relaxation time measurements of water in food. Food Chemistry, 37, 95-111.

Hills, B. P., & Le Floch, G. (1994). NMR-studies of non-freezing water in cellular plant-

tissue. Food Chemistry, 51, 331-336.

Hills, B. P., & Remigerau, B. (1997). NMR studies of changes in subcellular water

compartmentation in parenchyma apple tissue during drying and freezing.

International Journal of Food Science and Technology, 32, 51-61.

Hills, B. P., & Nott, K. P. (1999). NMR studies of water compartmentation in carrot

parenchyma tissue during drying and freezing. Applied Magnetic Resonance, 17, 521-

535.



Hills, B. P., & Clark, J. (2003). Quality assessment of horticultural products by NMR.

Annual Reports on NMR Spectroscopy, 50, 75-120.

Hills, B. P. (2006). Application of low field NMR to food science. Annual Reports on NMR

Spectroscopy, 58, 177-230.

Hills, B. P. (2008). NMR relaxation and diffusion studies of horticultural products. In G.

A. Webb (Ed.), Modern Magnetic Resonance: Part 1 (First edition, pp. 1721-1727).

Dordrecht, Netherlands: Springer.

Hinrichs R., Gotz J., Noll M., Wolfschoon A., Eibel H., & Weisser H. (2004).

Characterisation of the water-holding capacity of fresh cheese samples by means of

low resolution nuclear magnetic resonance. Food Research International, 37, 667-676.

Jung, K. H., Stroshine, R., Cornillon, P., & Hirst, P. M. (1998). Low field proton magnetic

resonance sensing of water core and internal browning in whole apples. Paper 98-6020.

ASAE, St. Joseph, MI.

Keener, K. M., Stroshine, R. L., & Nyenhuis, J. A. (1999). Evaluation of low field (5.40

MHz) proton magnetic resonance measurements of Dw and T2 as methods of

nondestructive quality evaluation of apples. Journal of the American Society for

Horticultural Science, 124, 289-295.

Kennedy, C. B., Balcom, B. J., & Mastikhin, I. V. (1998). Three-dimensional magnetic

resonance imaging of rigid polymeric materials using single-point ramped imaging

with T-1 enhancement (SPRITE). Canadian Journal of Chemistry, 76(11), 1753-1765.

Kerr, W. L., Clark, C. J., McCarthy, M. J., & de Ropp, J. S. (1997). Freezing effects in fruit

tissue of kiwifruit observed by magnetic resonance imaging. Scientia Horticulturae,

69, 169-179.

Koizumi, M., Naito, S., Haishi, T., Utsuzawa, S., Ishida, N., & Kano, H. (2006). Thawing

of frozen vegetables observed by a small dedicated MRI forfood research. Magnetic

Resonance Imaging, 24, 1111-1119.

Kotwaliwale, N., Curtis, E., Othman, S., Naganathan, G. K., Subbiah, J. (2012). Magnetic

resonance imaging and relaxometry to visualize internal freeze damage to pickling

cucumber. Postharvest Biology and Technology, 68, 22-31.

Kumpoun, W., Motomura, Y., & Harada, Y. (2003). Inhibition of aspergillus rot by sorbitol

in apple fruit with watercore symptoms. Postharvest Biology and Technology, 29, 121-

127.

Kuo, M. I., Anderson, M. E., & Gunasekaran, S. (2003). Determining effects of freezing

on pasta filata and non-pasta filata mozzarella cheeses by Nuclear Magnetic Resonance

Imaging. Journal of Dairy Science, 86, 2525-2536.

Laghi, L., Cremonini, M. A., Placucci, G., Sykora, S., Wright, K., & Hills, B. (2005). A

proton NMR relaxation study of hen egg quality. Magnetic resonance imaging, 23(3),

501-510.

Laghi, L., Babini, E., Bordoni, A., Ciampa, A., Danesi, F., Di Nunzio, M., Picone, G.,

Valli, V., & Capozzi, F. (2013). Time domain measurements and high resolution



spectroscopy are powerful nuclear magnetic resonance approaches suitable to evaluate

the in vitro digestion of protein-rich food products. In J. P. M. van Duynhoven, P. S.

Belton, G. A. Webb, & H. van As (Eds.), Magnetic Resonance in Food Sciences: Food

for Thought (First edition, pp. 201-212). Cambridge, UK: Royal Society of Chemistry.

Letal, J., Jirak, D., Suderlova, L., & Hajek, M. (2003). MRI ‘texture’ analysis of MR

images of apples during ripening and storage. LWT-Food Science and Technology, 36,

719-727.

Maas, J. L., & Line M. J. (1995). Nuclear magnetic resonance (NMR) inversion recovery

spin echo microimaging of fungus infections in strawberry fruit. Acta Horticulturae,

398, 241-248.

Manzocco, L., Anese, M., Calligaris, S., Quarta, B., Nicoli, M. C. (2012). Use of

monoglyceride hydrogel for the production of low fat short dough pastry. Food

Chemistry, 132, 175-180.

Marconi, S., Beni, C., Ciampa, A., Diana, G., Neri, U., Aromolo, R., Sequi, P., & Valentini,

M. (2010). Arsenic contamination in radish tuber investigated by means of MRI and

ICP OES. Journal of Food Quality, 33(4), 529-543.

Mariette, F., & Gollewet, G. (2001). Relationships between sensory texture of soft cheese

and MRI measurements. In Webb, G. A., Belton, P. S., Gil, A. M., & Delgadillo, Eds.

Magnetic Resonance in Food Science: A View to the Future. (First edition, pp. 67-74)

Cambridge, UK: Royal Society of Chemistry.

Mariette, F., & Lucas T. (2005). NMR signal analysis to attribute the components to the

solid-liquid phase present in mixes and ice creams. Journal of Agricultural and Food

Chemistry, 53, 1317-1327.

Marigheto, N., Duarte, S., & Hills, B. P. (2005). NMR relaxation study of avocado quality.

Applied Magnetic Resonance, 29, 687-701.

Marigheto, M., Venturi, L., & Hills, B. P. (2008). Two-dimensional NMR relaxation

studies of apple quality. Postharvest Biology and Technology, 48, 331-340.

Marques, J. P. M., Leloch, C., Wolff, E., & Rutledge, D. N. (1991a). Monitoring freeze-

drying by low resolution pulse NMR: determination of sublimation endpoint. Journal

of Food Science, 56, 1707-1710.

Marques, J. P. M., Rutledge, D. N., & Ducauze, C. J. (1991b). Low resolution pulse nuclear

magnetic resonance study of water equilibration in dried carrots. International Journal

of Food Science & Technology, 26, 173-183.

Martini, S., Herrera, M. L., & Marangoni, A. (2005). New technologies to determine solid

fat content on-line. Journal of the American Oil Chemists' Society, 82, 313-317.

McCarthy, M. J., Gambhir, P. N., Goloshevsky, A. G. (2006). NMR for food quality

control. In S. Stapf, & S. Han Editor (Eds.), NMR Imaging in Chemical Engineering

(First edition pp. 471-490). Weinheim, Germany: Wiley-VCH.

Melado Herreros, A., Munoz-García, M. A., Blanco, A., Val, J., Fernandez Valle, M. E.,

& Barreiro Elorza, P. (2012). Relationship between solar radiation on watercore on



apple fruit assessed with MRI. In International Conference of Agricultural

Engineering. CIGR-Ageng2012 Valencia (pp. 5).

Micklander, E., Peshlov, B., Purslow, P. P., & Engelsen, S. B. (2002). NMR-cooking:

monitoring the changes in meat during cooking by low-field 1H-NMR. Trends in Food

Science & Technology, 13(9), 341-346.

Mitchell, J., Gladden, L. F., Chandrasekera, T. C., & Fordham, E. J. (2014). Low-field

permanent magnets for industrial process and quality control. Progress in Nuclear

Magnetic Resonance Spectroscopy, 76, 1-60.

Mulas, G., Roggio, T., Uzzau, S., & Anedda, R. (2013). A new magnetic resonance

imaging approach for discriminating Sardinian sheep milk cheese made from heat-

treated or raw milk. Journal of Dairy Science, 96, 7393-7403.

Musse, M., De Guio, F., Quellec, S., Cambert, M., Challois, S., & Davenel, A. (2010).

Quantification of microporosity in fruit by MRI at various magnetic fields: comparison

with X-ray microtomography. Magnetic Resonance Imaging, 28, 1525-1534.

Nott, K. P., Evans, S. D., & Hall, L. D. (1999). Quantitative magnetic resonance imaging

of fresh and frozen-thawed trout. Magnetic Resonance Imaging, 17(3), 445-455.

Otero, L., & Prestamo, G. (2009). Effects of pressure processing on strawberry studied by

nuclear magnetic resonance. Innovative Food Science and Emerging Technologies, 10,

434-440.

Pedersen, H. T., Ablett, S., Martin, D. R., Mallet, M. J. D., & Engelsen S. B. (2003).

Application of the NMR-MOUSE to food emulsions. Journal of Magnetic Resonance,

165, 49-58.

Pereira, F. M. V., & Colnago, L. A. (2012). Determination of the moisture content in beef

without weighing using benchtop time-domain nuclear magnetic resonance

spectrometer and chemometrics. Food Analytical Methods, 5(6), 1349-1353.

Pereira, F. M. V., de Souza Carvalho, A., Cabeça, L. F., & Colnago, L. A. (2013).

Classification of intact fresh plums according to sweetness using time-domain nuclear

magnetic resonance and chemometrics. Microchemical Journal, 108, 14-17.

Petracci, M., Laghi, L., Rimini, S., Rocculi, P., Capozzi, F., & Cavani, C. (2014). Chicken

breast meat marinated with increasing levels of sodium bicarbonate. The Journal of

Poultry Science, 51(2), 206-212.

Pinter, M. D., Harter, T., McCarthy, M. J., & Augustine, M. P. (2014). Towards using

NMR to screen for spoiled tomatoes store in 1,000 l aseptically sealed, metal-lined

totes. Sensors, 14, 4167- 4176.

Povlsen, V. T., Rinnan, A., van den Berg, F., Andersen, H. J., & Thybo, A. K. (2003).

Direct decomposition of NMR relaxation profiles and prediction of sensory attributes

of potato samples. LWT-Food Science and Technology, 36, 423-432.

Prange, M., & Song, Y. Q. (2009). Quantifying uncertainty in NMR T2 spectra using

Monte Carlo inversion. Journal of Magnetic Resonance, 196(1), 54-60.



Raffo, A., Gianferri, R., Barbieri, R., & Brosio, E. (2005). Ripening of banana fruit

monitors by water relaxation and diffusion 1H-NMR measurements. Food Chemistry,

89, 149-158.

Rahman, M. S., & Conrad, O. P. (2007). Drying and food preservation. In M. S. Rahaman

(Ed.), Handbook of food preservation (Second edition, 403-432). Boca Raton, Florida:

CRC Press.

Ramos-Cabrer, P., Van Duynhoven, J. P. M., Timmer, H., & Nicolay, K. (2006).

Monitoring of moisture redistribution in multicomponent food systems by use of

magnetic resonance imaging. Journal of Agricultural and Food Chemistry, 54, 672-

677.

Ruan, R., Litchfield, J. B., & Eckoff, R. (1992) Simultaneous and nondestructive

measurement of transient moisture profiles and structural changes in corn kernels

during steeping using microscopic nuclear magnetice resonance imaging. Cereal

Chemistry, 69(6), 600-606.

Salerno, A., Pierandrei, F., Rea, E., & Sequi, P. (2005). Definition of internal morphology

and structural changes due to dehydration of radish (raphanussatinus l. cv. Suprella)

using Magnetic Resonance Imaging spectroscopy. Journal of Food Quality, 28, 428-

438.

Santagapita, P., Laghi, L., Panarese, V., Tylewicz, U., Rocculi, P., & Dalla, R. (2013).

Modification of transverse NMR relaxation times and water diffusion coefficients of

kiwifruit pericarp tissue subjected to osmotic dehydration. Food and Bioprocess


Sekiyama, Y., Horigane, A. K., Ono, H., Irie, K., Maeda, T., & Yoshida, M. (2012). T2

distribution of boiled dry spaghetti measured by MRI and its internal structure

observed by fluorescence microscopy. Food Research International, 48, 374-379.

Sequi, P., Dell’Abate, M. T., & Valentini, M. (2007). Identification of cherry tomatoes

growthorigin by means of magnetic resonance imaging. Journal of the Science of Food

and Agriculture, 87, 127-132.

Shaarani, S. M., Nott, K. P., & Hall, L. D. (2006). Combination of NMR and MRI

quantitation of structure and structure changes for convection cooking of fresh chicken

meat. Meat Science, 72, 398-403.

Shaarani, S. M. D., Cardenas-Blanco, A., Gao Amin, M. H., Soon, N. G., & Hall L. D.

(2010). Monitoring development and ripeness of oil palm fruit (Elaeis guneensis) by

MRI and bulk NMR. International Journal of Agricultural and Biology, 12, 101-105.

Simmons, A. L., & Vodovotz, Y. (2012). The effects of soy on freezable bread dough: A

magnetic resonance study. Food Chemistry, 135, 659-664.

Simons, R. K. (1968). The morphological and anatomical characteristics ofwatercorein

apples. Proceedings of the American Society for Horticultural Science, 93, 762-774.



Snaar, J. E. M., & Van As, H. (1992). Probing water compartments and membrane

permeability in plant cells by 1H NMR relaxation measurements. Biophysical Journal,

63, 1654-1658.

Stork, H., Gädke, A., & Nestle, N. (2006). Single-sided and semisingle-sided NMR sensors

for highly diffusive samples: application to bottled beverages. Journal of Agricultural

and Food Chemistry, 54, 5247-5252.

Taglienti, A., Massantini, R., Botondi, R., Mencarelli, F., & Valentini, M. (2009).

Postharvest structural changes of Hayward kiwifruit by means of magnetic resonance

imaging spectroscopy. Food Chemistry, 114, 1583-1589.

Thybo, A. K., Bechmann, I. E., Martens, M., & Engelsen, S. B. (2000). Prediction of

sensory texture of cooked potatoes using uniaxial compression, near infrared

spectroscopy and low field 1H NMR Spectroscopy. LWT-Food Science and


Thybo, A. K., Andersen, H. J., Karlsson, A. H., Donstrup, S., & Stodkilde-Jorgensen, H.

(2003). Low-field NMR relaxation and NMR-imaging as tools in differentiation

between potato sample and determination of dry matter content in potatoes. LWT-Food

Science and Technology, 36, 315-322.

Tylewicz, U., Panarese, V., Laghi, L., Rocculi, P., Nowacka, M., Placucci, G., & Dalla

Rosa, M. (2011). NMR and DSC water study during osmotic dehydration of Actinidia

deliciosa and Actinidia chinensis kiwifruit. Food Biophysics, 6(2), 327-333.

Tylewicz, U., Aganovic, K., Vannini, M., Toepfl, S., Bortolotti, V., Dalla Rosa, M., Oey,

I., & Heinz, V. (2016). Effect of pulsed electric field treatment on water distribution of

freeze-dried apple tissue evaluated with DSC and TD-NMR techniques. Innovative

Food Science & Emerging Technologies, 37, 352-358.

Valentini, M., Sequi, P., Ciampa, A., Ritota, M., Taglienti, A., Cozzolino, S., Conte, L., &

Terlizzi, M. (2009). Qualità tramite Risonanza Magnetica per Immagini: valutazione

della conservazione e dei trattamenti agronomici. Italus Hortus, 16(5), 324-328.

Van Duynhoven, J., Voda, A., Witek, M., & Van As, H. (2009). Time-domain NMR

applied to food products. Annual Reports on NMR Spectroscopy, 69, 145-197.

Vandeginste, R. G. M., Massart, D. L., Buydens, L. M. C., De Jong, S., Lewi, P. J., &

Smeyers-Verbeke, J. (1998). Handbook of Chemometrics and Qualimetrics: part B

(First edition). Amsterdam, Netherlands: Elsevier Science Publisher.

Veliyulin, E., van der Zwaag, C., Burk, W., Erikson, U. (2005). In vivo determination of

fat content in Atlantic salmon (Salmo salar) with a mobile NMR spectrometer. Journal

of the Science of Food and Agriculture, 85, 1299-1304.

Venkataramanan, L., Song, Y. Q., & Hurlimann, M. D. (2002). Solving Fredholm integrals

of the first kind with tensor product structure in 2 and 2.5 dimensions. IEEE

Transactions on Signal Processing, 50, 1017-1026.

Wang, N., & Brennan, J. G. (1995). Changes in structure, density and porosity of potato

during dehydration. Journal of Food Engineering, 24, 61-67.



Wang, S. Y., Wang, P. C., & Faust, M. (1988). The non-destructive detection of watercore

in apple with Nuclear Magnetic Resonance Imaging. Scientia Horticulturae, 35, 227-

234.

Xin, Y., Zhang, M., & Adhikari, B. (2013). Effect of trehalose and ultrasound-assisted

osmotic dehydration on thestate of water and glass transition temperature of broccoli

(Brassicaoleracea L. var. botrytis L.). Journal of Food Engineering, 119, 640-647.

Xu, B., Zhang, M., Bhandari, B., Cheng, X. F., & Sun, J. (2015). Effect of ultrasound

immersion freezing on the qualityattributes and water distributions of wrapped red

radish. Food and Bioprocess Technology, 8, 1366-1376.

Xu, Z., Morris, R. H., Bencsik, M., & Newton, M. I. (2014). Detection of virgin olive oil

adulteration using low field unilateral NMR. Sensors, 14, 2028-2035.

Zion, B., Chen, P., & McCarthy, M. J. (1995). Detection of bruises in magnetic resonance

images of apples. Computers and Electronics in Agriculture, 13, 289-299.

Zou, Y., Xie, R., Ding, Y., & Arad, A. (2016). Determining Uncertainty in NMR T2

distribution using frequentist method. Applied Magnetic Resonance, 47(10), 1081-

1094.


NMR APPLICATIONS IN FOOD ANALYSIS PART B 7_NMR Applic… · 256 Noemi Proietti, Donatella Capitani,...

Documents

Transcript of NMR APPLICATIONS IN FOOD ANALYSIS PART B 7_NMR Applic… · 256 Noemi Proietti, Donatella Capitani,...