Volume 52 b Assessment of Potato Dry Matterpdf

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Assessment of Potato Dry Matter

Concentration Using Short-WaveNear-Infrared Spectroscopy

P. P. Subedi & K. B. Walsh

Received: 15 September 2006 /Accepted: 22 December 2008 /

Published online: 7 February 2009

# EAPR 2009

Abstract The utility of short-wavelength near-infrared spectroscopy (over the

wavelength region 750 – 950 nm), used in a partial transmittance optical geometry,

was assessed as a means of estimating the dry matter concentration of potato tubers.

The sampling optics did not involve contact with the sample, and could be used on a

moving stream of product. A prediction accuracy of R2 (correlation coefficient of

determination) of 0.85 with a root mean square error of prediction (RMSEP) of

1.52% for intact, whole tubers and R2=0.95 and RMSEP=0.50% for sliced tubers

was achieved. We conclude that short-wavelength near-infrared technology using a

partial transmittance optical sampling geometry can be a useful tool for rapid

assessment of tuber dry matter concentration prior to processing.

Keywords Dry matter . Herschel region . Internal quality assessment .

Near-infrared spectroscopy . Non-destructive . Potato chipping quality

Abbreviations

DM Dry matter

NIR Near infrared NIRS Near-infrared spectroscopy

PLS Partial least squares

RMSEC Root mean square error of calibration

RMSEP Root mean square error of prediction

SD Standard deviation

SWNIR Short-wave near infrared

Potato Research (2009) 52:67 – 77

DOI 10.1007/s11540-008-9122-1

P. P. Subedi (*) : K. B. WalshPlant Sciences Group, Building 7, Central Queensland University, Rockhampton, QLD 4702,

Australia

e-mail: [email protected]


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Introduction

Water, sugar and starch concentration are important quality parameters for potato

destined for frying, ideally assessed for intact tubers rather than for sliced,

homogenized or oven-dried and ground material. In practice, dry matter (DM)concentration is a useful index for potato quality, containing information on both

water and carbohydrate concentration. Water and carbohydrate concentration are

prime candidates for measurements using near-infrared spectroscopy (NIRS; over

the range 750 – 2,500 nm), with vibration of C-H and O-H bonds responsible for

strong absorption features. For assessment of bulk tissue, however, operation within

the shorter-wavelength region (‘Herschel’, or short-wave near infrared, SWNIR,

defined as 750 – 1,100 nm) is appropriate, as this wavelength region encompasses the

higher-order overtones of the OH and CH stretching and combination vibrations.

These higher-order overtones have lower absorption coefficients than the lower-order overtones and fundamental vibrations, thus allowing penetration to some depth

of the tissue. The third and second overtone bands of OH stretching occur at around

760 and 980 nm, a combination band of the OH stretch occurs around 840 nm, and

the third and second overtones of the CH stretch occur around 910 and 1,100 nm,

respectively.

Inexpensive silicon photodiode detectors are appropriate for equipment operating

in the SWNIR wavelength region. Filter-based instrumentation using a single

photodiode, which detects at only a few wavelengths, is well established in

commercial use for measurement of moisture concentration of processed foods (e.g., biscuits; MM710 from NDC Infrared Engineering, Malden, UK, http://www.

ndcinfrared.com). Array photodiodes coupled with dispersive optics have been

employed for in-line sorting of fresh fruit, at speeds of up to ten items per second

(e.g., Colour Vision Systems, Bacchus Marsh, Australia, http://www.cvs.com.au;

Compac, Auckland, New Zealand, http://www.compac.com), and in a portable, hand-

held format (e.g., Integrated Spectronics, Sydney, Australia, http://www.intspec.com;

Fantech, Hamamatsu, Japan; http://www.fantech.com.jp). Such technology may be

applicable to potato quality assessment, in terms of on-line inspection of individual

tubers or sliced tubers, or in terms of lot assessment of tubers in the field.

‘Proof of principle’ for the application of NIRS to potato assessment has been

provided by a number of authors. In reviewing prior work, there are four major

considerations — the wavelength range and the speed of operation of the

instrumentation employed, the optical geometry of the light source-sample-detector,

and the distinction between calibration and validation statistics, with validation

entailing use of the model to predict attribute levels in samples of a population

independent of that used for calibration.

Previous work (listed below) has been conducted on research-grade, bench-top

instrumentation (principally the NIRSystems 6500 and 5500, from FOSS, Denmark,

http://www.foss.dk ; e.g., Hartmann and Buning-Pfaue 1998; Scanlon et al. 1999).

Such instrumentation is not suited to on-line or in-field application, in terms of cost

and speed of operation. In general, work targeting bulk tuber tissue has appropriately

included consideration of the SWNIR wavelength region. However, reflectance

optics have been used in a number of cases (e.g., Scanlon et al. 1999). Reflectance

optics involve the detector receiving radiation from an illuminated area of the

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sample, such that both specular and diffusely reflected light are received (with the

former carrying no information about the sample). Spectra collected with reflectance

optics are sensitive to a change in sample surface characteristics, and therefore well-

prepared (e.g., homogenized) samples should be used. For samples such as intact

potato tubers, an optical geometry that prevents surface (specular) reflections fromreaching the detection system is to be preferred (e.g., ‘transmittance’ or ‘ partial

transmittance’ optics).

The near infared (NIR) based determination of potato DM concentration was first

reported by Dull et al. (1989), using reflectance optics. This report was followed by

that of Hartmann and Buning-Pfaue (1998), who employed reflectance measure-

ments in the 1,100 – 2,500-nm range with homogenized peeled potatoes. It was noted

that DM calibration performance was highly dependent on the cultivar measured. As

application to a homogenized sample has little practical relevance in processing,

Scanlon et al. (1999) used excised ‘coins’

(48-mm diameter and 10 mm thick, periderm removed) of potato tissue, collecting reflectance spectra over the

wavelength region 770 – 2,500 nm. Typical calibration statistics on DM concentration

of R2c ¼ 0:75 0:84 and root mean square error of calibration (RMSEC) of 1.1 – 1.2% DM were reported for a model developed across several cultivars. The model

was reported to be robust in predicting DM concentration in populations of sliced

tubers varying in cultivar, time of harvest and storage conditions. For example,

statistics for the prediction of tubers from a different year of harvest compared with

those for the calibration group were reported as R2 p ¼ 0:67 0:77 and root mean

square error of prediction (RMSEP) of 1.2 –

1.3% DM. Note, however, that an R

2

of 0.75 is required for reliable sorting of a group into two categories (i.e. from

rearrangement of Eq. 1, when R2=0.75, the bias-corrected standard deviation (SD)

equals twice the bias-corrected RMSEP).

R2 ¼ 1 bias corrected RMSEP =SDð Þ2 ð1Þ

Scanlon et al. (1999) also recognized that ‘ potato processors are more interested

in how accurately whole tuber dry matter is predicted’, as opposed to the prediction

of DM concentration of a coin of tissue. To address this issue, the authors suggested

use of a (cultivar-specific) relationship between whole-tuber and coin DM (Pritchard

and Scanlon 1997).

Mehrubeoglu and Cote (1997) indicated that the sugar concentration of thin slices

of potato could be determined using NIRS in transmittance geometry. However, only

calibration results were presented, with no prediction of an independent data set

attempted; thus, the results may represent overfitting of the data. In contrast, Scanlon

et al. (1999) reported that a reflectance NIRS technique was not useful for prediction

of sugar (glucose or fructose) concentration in potato ‘coins’. The conclusion that

sugar assessment is not possible is not surprising, given the relatively low sugar

concentration of the potato tuber (approximately 1 – 2% w/v of tissue juice, compared

with starch at 15 – 25% w/w, fresh weight basis), compared with the typically

reported RMSEP for the NIRS technique (around 0.5% w/v of tissue juice; Walsh et

al. 2004).

Boeriu et al. (1998) reported the use of NIR reflectance spectra (1,100 –

2,500 nm) of sliced potato for assessment of the sensory parameters of

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‘moistness’, ‘waxiness’, firmness and ‘mealiness’. However, again only the

results of a calibration exercise were reported, without testing of this model

against an independent validation set.

The DM calibration performances reported by Scanlon et al. (1999) are

encouraging, warranting further work in terms of use of instrumentation suited toon-line and in-field use, and in terms of avoiding use of reflectance optics, given the

sensitivity of models based on such spectra to changes in surface conditions of the

sample. However, the use of a 180° transmission geometry (i.e. angle between light

source, sample and detector of 180°) is also inappropriate, given that the optical

density of potato tissue would mean unacceptably low signal levels (with consequent

noise issues), unacceptably long integration times, or unacceptably high illumination

levels (and consequent safety issues).

A non-contact ‘ partial transmittance’ geometry (sometimes termed ‘interactance’

geometry), as proposed by Greensill and Walsh (2000), coupled with a silicon photodiode array detector, has been employed for the assessment of fruit for total

soluble solids and DM concentration of intact fruit (e.g., Walsh et al. 2004). This

geometry entails passage of light from the light source through part of the sample to

the detector. This type of instrumentation is commercially available for on-line

sorting of up to ten items of fruit per second (e.g., from Colour Vision Systems or

Compac), and in hand-held units appropriate for use in the field (e.g., from

Integrated Spectronics).

On the basis of the available literature, we conclude that SWNIRS may be

appropriate for the application of measurement of the DM, but not reducing sugar,concentration of potato tubers destined for processing. The aim of the present

research was to trial the non-contact spectrometer configuration described by

Greensill and Walsh (2000) for DM application.

Materials and Methods

Experimental Design

Potato tubers (Solanum tuberosum) were obtained from a retail market in

Rockhampton in June 2004.

Exercise 1 was undertaken to establish the distribution of DM concentration

within tubers. It utilized a total of ten tubers from six cultivars (one each of Nicola,

Spunta and Golden Delight, two Sebago and five Russet Burbank tubers). The tubers

were cleaned in tap water and dried at room temperature before being cut

longitudinally. A 20-mm-thick slice was taken from the middle part of each tuber,

and this slice was further divided into 11 subdivisions. DM concentration of these

parts was determined following drying at 65 °C for 48 h.

Three sets of 50 tubers (40, four, two, two and two tubers of cvs. Russet Burbank,

Sebago, Spunta, Nicola and Golden Delight, respectively) were acquired for use in

exercises 2, 3 and 4, as described below.

The utility of SWNIRS for the measurement of DM concentration of

stationary potato samples was considered in exercises 2 and 3. In each exercise,

two spectra were sequentially acquired from the same location on intact tubers

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(n =50) in (1) an unwashed condition, (2) following washing and (3) after removal

of the periderm using a potato peeler. Tubers were sealed with a plastic sheet

(Gladwrap™) following peeling, to minimize water loss and delay the browning

process. Plastic was removed for spectral acquisition. A longitudinal slice

approximately 10 mm thick was then taken from the middle part of each tuber.Following spectral acquisition, DM concentration was assessed by oven drying of a

tissue piece of approximately 20 mm×20 mm cut from the area of spectral

acquisition.

Two exercises were undertaken to test the utility of SWNIRS for the measurement

of DM on a moving stream of sliced potato tuber. For exercise 3, the remaining

(peeled and cut) tuber material from exercise 2 as described above was sliced into

different shapes (flat, wedge, cubes and end caps) and scattered randomly over 11

plates to approximately 30-cm diameter and 10 mm thick before spectral acquisition.

For exercise 4, another set of 50 tubers was sliced and presented on plates ( n=31). In both exercises, DM concentration of the entire plate of tuber material was

determined, again by oven drying.

Spectral Acquisition and Reference Method

Spectra were acquired using a Carl Zeiss (Jena, Germany) MMS1/NIR-enhanced

spectrometer (310 – 1,133 nm) in the ‘non-contact ’ configuration reported by

Greensill and Walsh (2000), in which a single 100-W tungsten halogen lamp was

used for illumination and the optical pickup for the detector was placed in front of the lamp, creating a shaded area on the sample (tubers). A collimating lens was used

at the front of the detector (15-mm diameter). A Teflon plate was placed in the

position of the sample, 15 mm below the detector optical pickup, to act as a white

reference (W ). The detector optical pickup was occluded to allow acquisition of a

dark spectrum ( D).

Spectral acquisition involved the recording of the output of the 16 bit analogue to

digital converter output as a ‘raw energy’ spectrum. The absorption spectrum of a

given sample was calculated as A = − log(S − D)/(W − D), where S is the sample

‘raw energy’ spectrum at a given wavelength.

In exercises 2 and 3, spectra were acquired of individual tubers at a mid-length

position along the tuber. Two spectra were taken from the same region of the tuber.

Integration time was varied by sample type, being adjusted to achieve a signal level

at approximately two thirds of the detector maximum count level. Integration times

of 100, 20, 15 and 10 ms were employed, respectively, for unwashed, clean, peeled

and sliced samples.

To simulate material moving on a conveyor, spectra were acquired by

continuous movement of a plate covered in sliced tubers beneath the detector,

averaging 500 spectra per plate and using a 10 ms integration time (n =11,

exercise 3; n = 31, exercise 4). This spectral collection activity was undertaken

twice in exercise 4. DM concentration of 20 mm×20 mm×10 mm tissue blocks

was assessed in exercise 2, and of the entire ‘ plate’ sample for spectra in exercises

3 and 4.

DM concentration was assessed by weight difference following drying to constant

weight at 65 °C in a fan-forced oven (72 h).

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Data Acquisition, Preprocessing and Analysis

Spectral data were collected using an in-house software package (Hortical version

9.00) with count data converted into absorbance. The Unscrambler version 7.6

chemometric software package (Camo, Sweden) was used for preprocessing of data(e.g., second derivative calculated using a Savitzky-Golay second order polynomial

fit using four pixels either side of each pixel). The Unscrambler ™ outlier identification

routine was used, with samples with values greater than 0.15 × 10−10 and 3.0 for X

and Y residual sample variance, respectively, removed from the partial least squares

(PLS) regression calibration process.

The RMSEC of models developed using the second derivative of absorption data

was slightly lower than that of models based on the first-derivative or raw absorption

data (data not shown). All models reported in this report were therefore based on

second-derivative data. Model performance is reported in terms of cross-validation(using a leave-one-out cross-validation approach, with Rcv

2 and root mean square

error of cross-validation reported) and in terms of prediction of a set of samples

independent of those used in calibration ( R p2, RMSEP and bias reported). Bias is

calculated as the mean of the predicted set minus the mean of the reference values.

Results and Discussion

Reference and Optical Sampling

In tubers of all cultivars considered, the DM concentration of the cortex was higher

than that of the pith, with little difference and no consistent trend in DM

concentration of the outer cortex at different positions around the tuber (exercise

1, e.g., for Russet Burbank tubers, 17.6% at the centre, 23.4 – 25.5% DM in outer

regions; data not shown). This distribution pattern is well known (e.g., Pritchard and

Scanlon 1997).

The non-contact-geometry optical probe used in the current study optically

samples tissue to approximately 10 – 20-mm depth (Walsh et al. 2004); therefore, the

spectra acquired of whole tubers are effectively only of cortical, and not pith, tissue.

Given that the DM concentration of the tuber cortex is relatively uniform, it is

recommended that the orientation of the tuber with respect to the optical probe is not

important. However, to use this technology to estimate whole-tuber DM concentra-

tion, the user would need to calibrate the outer-tissue DM concentration to the

whole-tuber DM concentration (as undertaken by Pritchard and Scanlon 1997).

Interpretation of Spectra

While SWNIR second derivatives of absorption spectra of unwashed tubers were

clearly different from those of clean, peeled or sliced potato tubers, all spectra

demonstrated negative peaks around 740, 840 and 960 nm (Fig. 1). These features

are attributed to water (third and second overtones of OH stretching and an OH

combination band). Tubers could be roughly differentiated on the basis of their DM

concentration by the (second-derivative) absorption at these wavelengths (Fig. 2).

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Starch, which contains CH and CH2 groups, is a major component of the potato

tuber. However, there was no obvious contribution from the third overtone of the CH

and CH2 stretching vibration (expected at around 910 nm) notable in the absorption

spectra.

PLS Regression Modelling of DM Determination

The inclusion of spectral data from wavelength regions that do not carry information

related to the attribute of interest can decrease multivariate model performance.

Therefore, reduction of the data set to spectrally relevant regions is a useful first step.

However, multivariate models linking spectra to a chemical attribute in biological

samples often weight a shoulder of the absorption feature of interest, rather than the

peak of the feature, owing to interference from other absorption features of other

attributes. Thus, the data set should not be reduced to specific wavelengths related to

the peak of absorption features of the attribute of interest.

An iterative procedure was used to assess the optimum wavelength range for PLS

regression modelling of DM concentration, using different start and finish

Fig. 2 Mean (n=6) second de-

rivative of absorption spectra for

intact washed tubers with high

( solid line; 26.4%), medium

(dotted line; 20.9%) and low

(dashed line; 15.4%) dry matter

concentration

Fig. 1 Mean (n=100) second

derivative of absorption spectra

for unwashed ( solid line),

washed (dotted line), peeled

(dashed line) and sliced

(dash-dotted line) tubers

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wavelengths (following the method of Guthrie et al. 2005; data not shown). The

750 – 950-nm region was selected. Various preprocessing routines (first and second

derivatives, derivative window) were also trialled (data not shown). A (Savitzky-

Golay second-order) second derivative with a window of four pixels either side of

the point of interest (i.e. a window of 25 nm) was selected for all work reported here.

The DM concentration PLS model regression coefficients were similar for the

different sample presentation types (Fig. 3). In general, the wavelengths of highest

regression coefficient values were not aligned with wavelengths of known

spectroscopic significance, except that a strong negative peak was apparent at

910 nm, consistent with an influence from the CH third overtone feature.

Presumably the PLS model weights the shoulder of absorption peaks related to the

attribute of interest, making interpretation difficult.

The PLS regression calibration results for DM concentration using the non-

contact interactance probe were highly encouraging. A cross-validation Rcv 2 greater

than 0.85 was obtained for all sample presentation styles, including unwashed tubers

(Table 1). As expected, the highest correlation ( R2cv > 0:9) was obtained fromsamples with the skin removed (i.e. peeled or sliced tubers).

The model statistics for the moving sliced samples are not directly comparable tothose for the stationary samples as different samples were involved (thus a different

mean and SD applies). The moving-sample calibration statistics involved a lower

Table 1 Statistics for partial least squares (PLS) regression models on dry matter (DM) concentration of

potato tubers (exercise 2)

Population n Mean SD Factors Outliers Rcv 2 RMSECV

Unwashed tubers 100 21.2 2.6 5 10 0.87 0.86

Washed tubers 100 21.2 2.5 5 7 0.87 0.87Peeled tubers 100 21.2 2.5 4 0 0.92 0.70

Sliced tubers 100 21.2 2.6 4 6 0.92 0.72

All types (combined) 400 21.2 2.5 8 16 0.83 0.98

Moving sliced tubers 50 22.5 1.1 6 4 0.85 0.42

Sample number n refers to the number of spectra (two spectra per tuber). Values for the mean, standard

deviation (SD) and root mean square error of cross-validation ( RMSECV ) have units of percent DM

Fig. 3 Partial least squares

model regression coefficients for

potato dry matter concentration,

based on the second derivative

of absorption spectra collected

from unwashed ( solid line),washed (dotted line) and sliced

(dashed line) tubers

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Rcv 2 than those of the stationary samples, in part a function of the lower SD of DM

concentration of this set (1.1% compared with 2.6%). Nonetheless, the result is

encouraging, and suggests the technology could be used for in-line assessment of

average moisture concentration of a process stream of sliced tubers, with the

assessed moisture level used in process control operations. For example, given the

low DM concentration of the pith tissue of the tuber, it may be appropriate to screen

for slices taken on a radial longitudinal plane through the tuber, using air-jet

technology to eject samples from the processing stream.

The strength of a model, however, is best measured by its ability to predict

samples from populations independent of the calibration population. A model was

developed using spectra of Russet Burbank tubers, and was used in prediction of

spectra of other cultivars (from exercise 2) (Table 2). Prediction performances (interms of both R p

2 and RMSEP) were acceptable ( R2 p > 0:8, RMSEP within the range1.1 – 1.6% DM) for all sample presentation types. As expected, the best prediction

result ( R2 p ¼ 0:90) was obtained with peeled samples.As expected, when a model based on spectra of moving samples was used for the

prediction of a second set of spectra collected from the same samples, the prediction

results were good ( R2 p > 0:92) (exercise 4). When a model created on the basis of exercise 3 spectra was used in the prediction of exercise 4 samples, and vice versa,

acceptable prediction results were also obtained ( R2 p > 0:8) (Table 3).

These prediction results are superior to those reported in the literature (e.g.,Scanlon et al. 1999). This outcome is ascribed to the use of a more appropriate

optical geometry (partial transmittance compared with reflectance). As noted

previously, models based on reflectance spectra are expected to be more sensitive

Table 2 Prediction statistics for PLS regression models based on ‘static’ (non-moving) samples

(exercise 2)

Sample type R p2 RMSEP Bias

Dirty 0.85 1.52 −

0.92Clean 0.81 1.64 −1.12

Peeled 0.90 1.13 0.38

Sliced 0.86 1.08 −0.22

Calibration models were based on spectra of Russett Burbank tubers, and were used in prediction of

spectra of other cultivars. The mean and SD of the prediction set was 18.8±2.7% DM. Values of the root

mean square error of prediction (RMSEP) and bias have units of percent DM

Table 3 Prediction statistics for PLS regression models based on moving samples (exercises 3 and 4)

Calibration Set Prediction Set R p2 RMSEP Bias

4A 4B 0.92 0.77 0.33

4B 4A 0.96 0.50 0.984B 3 0.83 1.23 0.60

3 4B 0.81 1.08 0.54

The first column refers to the spectral data set used for model development, while the second column

refers to the data set used for prediction (from exercises 3 and 4, with two sets available for exercise 4).

The mean and SD of DM concentration reference values in exercise 3 was 22.5±1.1%, and in exercise 4

was 22.1±2.4%. Values of the RMSEP and bias have units of percent DM

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to changes in surface conditions, and thus the extent of specular, compared with

diffuse, reflection.

Conclusion —

Instrumentation

SWNIR-based technology has been advocated as a potential tool for rapid

assessment of DM as an internal quality attribute, an attribute of significance to

the potato processing industry. The lack of current commercial adoption of the

technique by the potato industry is at least in part due to lack of appropriate

instrumentation. The instrumentation trialled has been demonstrated to support an

acceptable level of prediction accuracy for whole and sliced tubers. This type of

system can be employed in either an on-line configuration, capable of sorting up to

ten items per second, or in a hand-held format, suitable for in-field work. Futurework should consider further the robustness of model predictions across populations

of tubers from different growing conditions and different varieties.

Other partial transmittance (with light transmitted through the sample in a

geometry involving an angle of less than 180° between the light source, sample and

detector) or full transmittance (involving 180° geometry) optical configurations

should also be trialled for this application as developments in detector sensitivity

occur. For example, Chalukova-Dimitrova et al. (1994) and Krivoshiev et al. (2000)

proposed a method involving assessment of both reflectance and transmission of a

given potato to ‘

virtually’ remove the contribution of skin (and surface dirt) from thetuber spectra. A prototype based on this principle has been developed by VTT

Technical Research Centre of Finland (http://www.vtt.com). In its present form, this

method is based on a few (two or three) wavelengths, and is thus unable to be used

for multivariate spectroscopy. However, DM calibrations could conceivably be based

on measurements of only a few wavelengths (as claimed for potato ‘coins’ by

Scanlon et al. (1999)), although this approach is more likely to be successful with

homogenous samples than for heterogeneous samples such as intact potato tubers.

Acknowledgement We acknowledge the funding support of Hortical and Horticulture Australia Ltd.

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12:141 – 148

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http://localhost/var/www/apps/conversion/tmp/scratch_4/dx.doi.org/10.1071/AR05123http://localhost/var/www/apps/conversion/tmp/scratch_4/dx.doi.org/10.1007/BF02358965http://localhost/var/www/apps/conversion/tmp/scratch_4/dx.doi.org/10.1002/(SICI)1097-0010(199904)79:5%3C763::AID-JSFA250%3E3.0.CO;2-Ohttp://localhost/var/www/apps/conversion/tmp/scratch_4/dx.doi.org/10.1002/(SICI)1097-0010(199904)79:5%3C763::AID-JSFA250%3E3.0.CO;2-Ohttp://localhost/var/www/apps/conversion/tmp/scratch_4/dx.doi.org/10.1002/(SICI)1097-0010(199904)79:5%3C763::AID-JSFA250%3E3.0.CO;2-Ohttp://localhost/var/www/apps/conversion/tmp/scratch_4/dx.doi.org/10.1002/(SICI)1097-0010(199904)79:5%3C763::AID-JSFA250%3E3.0.CO;2-Ohttp://localhost/var/www/apps/conversion/tmp/scratch_4/dx.doi.org/10.1007/BF02358965http://localhost/var/www/apps/conversion/tmp/scratch_4/dx.doi.org/10.1071/AR05123

Volume 52 b Assessment of Potato Dry Matterpdf

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Transcript of Volume 52 b Assessment of Potato Dry Matterpdf