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Use of near-infrared spectroscopy and chemometrics for the non-destructive identification of concealed damage in raw almonds (Prunus dulcis) Cristian Rogel-Castillo, Roger B. Boulton, Arunwong Opastpongkarn, Guangwei Huang, and Alyson Elayne Mitchell J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.6b01828 • Publication Date (Web): 16 Jun 2016 Downloaded from http://pubs.acs.org on June 17, 2016

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Journal of Agricultural and Food Chemistry

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Use of near-infrared spectroscopy and chemometrics for the non-destructive identification

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of concealed damage in raw almonds (Prunus dulcis)

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Cristian Rogel-Castillo1, Roger Boulton2, Arunwong Opastpongkarn1, Guangwei Huang3 and

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Alyson E. Mitchell1*

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Avenue, Davis, CA 95616, United States; 2 Department of Viticulture and Enology, University

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of California Davis, One Shields Avenue, Davis, CA 95616, United States; 3Almond Board of

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Department of Food Science and Technology, University of California Davis, One Shields

California, 1150 9th Street, Suite 1500, Modesto, CA 95354 United States;

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*Address correspondence to this author at Department of Food Science and Technology,

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University of California, Davis, CA 95616 [telephone (530) 304-6618; fax (530) 752-4759; e-

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mail [email protected]]

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ACS Paragon Plus Environment


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Abstract

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Concealed damage (CD) is defined as a brown discoloration of the kernel interior

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(nutmeat) that appears only after moderate to high heat treatment (e.g. blanching, drying,

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roasting, etc.). Raw almonds with CD have no visible defects before heat treatment. Currently

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there are no screening methods available for detecting CD in raw almonds. Herein, the feasibility

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of using Near-Infrared Spectroscopy (NIR) between 1125–2153 nm for the detection of CD in

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almonds is demonstrated. Almond kernels with CD have less NIR absorbance in the region

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related with oil, protein and carbohydrates. Using partial least square regression and discriminant

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analysis (PLS-DA) and by selecting specific wavelengths, three classification models were

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developed. The calibration models have false positive and false negative error rates ranging

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between 12.4-16.1% and 10.6-17.2%, respectively. The percent error rates ranged between 8.2-

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9.2%. Second derivative pre-processing of selected wavelength resulted in the most robust

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predictive model.

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Keywords

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Prunus dulcis; Almond, Concealed Damage; Near Infrared Spectroscopy; Partial Least Square

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Regression; Discriminant Analysis.

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Introduction

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Concealed damage (CD) in raw almonds (Prunus dulcis (Mill.) D.A. Webb) is defined by

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the industry as a brown discoloration of the kernel interior (nutmeat) that appears only after

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moderate to high heat treatment (e.g. blanching, drying, roasting, etc.,) as shown in Figure 1. CD

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may develop anytime during harvest when rain occurs, or after harvest when kernels are in

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windrows or stockpiles and exposed to warm and moist environments.1,2 Raw almond kernels

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with CD, have no visible defects on the interior or exterior surface of the kernel. Additionally,

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there are no visible signs of CD on the surface of whole roasted kernels.3 CD is frequently

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associated with a strong bitter flavor(s) that can result in immediate consumer rejection.1

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Currently there are no screening methods available for detecting CD in raw almonds, or other

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nuts affected by CD, and processors often do not realize nuts are damaged until after they have

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been roasted.1 Under current production practices, the most common methods for detecting CD

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involve visual inspection of roasted almonds after they are split open. Kernels with a “dark

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brown” color over ~50% of the interior of the kernel are considered to have CD.4 A similar

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approach is used for hazelnuts.5 Visual inspection and manual sorting is time-consuming,

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subjective, labor intensive and cannot be used to identify nuts with CD before heat treatments.

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This can result in significant product loss.

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The current hypothesis is that the browning associated with CD is related to the Maillard

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reaction. Moisture can induced the hydrolysis of carbohydrates and potential availability of

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reducing sugars for Maillard browning reactions. For example, in macadamia nuts exposed to

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moisture during harvesting, increased levels of reducing sugars were observed in nuts with

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internal browning.6 Similar observations were made in hazelnuts7 and in almonds exposed to



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simulated rainfall.2 In more recent studies, elevated levels of volatiles related to lipid oxidation

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and amino acid degradation were observed in almonds with CD.8 Both lipid oxidation products

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and protein degradation products can serve as reactants in the Maillard browning reaction.

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Near-infrared spectroscopy (NIR) is a rapid and effective method for screening foods for

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specific chemical and physical characteristics.9 NIR is advantageous as a screening method as it

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is non-destructive, can be used on whole foods, and produces no waste. The NIR spectral region

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(720 to 2500 nm) is ideally suited for foods as it contains absorbance bands that result primarily

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from three chemical bonds: C-H (fats, oil, hydrocarbons), O-H (water, alcohol) and N-H

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(protein). NIR spectroscopy is increasingly considered one of the more promising in-line

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detection methods for rapidly measuring specific chemical properties of food.6 It has been

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successfully applied in detecting quality defects in macadamia kernels7, walnuts10, chestnut11 - 12,

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hazelnuts13

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including oleic and linoleic acid content in peanut seed16, acidity and water content in

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hazelnuts17, and characterization of shea tree nut fat profiles.18

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, and soybean seed.15 It has also been employed for food composition analysis

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Pearson (1999) was the first to recognize the use of NIR spectroscopy for the

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identification of CD in raw almonds4, 19 and evaluated the transmission spectrum from 700–1400

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nm in almonds soaked in water, and dried but not roasted. In these studies, almonds were either

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soaked for 30 minutes and exposed to 95% relative humidity for 30 hours (short moisture), or

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soaked for 60 minutes and exposed to 95% relative humidity for 60 hours (long moisture).

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Almonds were then dried at either 55 or 110°C. The higher temperature and shorter soak times

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produced the greatest amount of CD. Almonds with CD had enhanced absorption at 930 nm (oil

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absorption band). Raw almonds with CD could be distinguished from normal almonds at an error

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of 12.4% by using principal components of the absorbance, first derivative and second derivative


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spectra between 1000-1300 nm. Pearson (1999) recognized that collecting the NIR spectra over

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the full transmission range would be too slow to achieve desired inspection rates of 40 nuts/s and

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therefore tested the feasibility of using just 6 light emitting diodes at 660, 830, 880, 890, 940,

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and 950 nm19. These data resulted in a classification error rate of 14.3% for the validation set.

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More recently, Nakariyakul20

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transmission and focusing on a sub-set of absorbing bands (760, 920, 935, and 970 nm) with a

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false negative error rate of 14.81%. Almonds used in this study were generated by Pearson

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(1999) as described above.

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achieve a higher classification rate using hyperspectral

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Herein, we present the development of a prediction model for the classification of

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almonds with CD using reflectance NIR in the extended range of the NIR spectrum (1125 – 2153

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nm) and by employing data pre-processing and partial least square discriminant analysis (PLS-

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DA). Almonds evaluated in this study were exposed to controlled humidity environments that

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produced an internal nut moisture content of ~5% (control), 8% (mild CD) and 11% (100% CD).

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The percent CD in the raw almonds was validated using colorimetry as described previously8.

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Developing a rapid in-line screening method for detecting CD in raw almonds is a critical

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step towards improving quality control measures in almond processing and offers the advantage

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of sorting almonds with CD into product lines that do not require roasting or other heat

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treatments.

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Materials and Methods

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Sample preparation

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Dehulled raw kernels (100 lbs., var. Nonpareil) were supplied by the Nickels Soil lab

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(Arbuckle, CA) in September 2013. Individual vessels containing ~100 gm were exposed to

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conditions that produced an internal kernel moisture of 5% (actual 5.4 ± 0.2), 8% (actual 8.6 ±

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0.7) or 11% (actual 10.4 ± 1.5) in a controlled atmosphere (Thermo Scientific, Marietta, OH) at

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45 ± 2°C. Under these conditions, CD is observed after 24 hours. The moisture content of the

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almonds was validated gravimetrically by drying samples (~1 g) at 95-105°C under vacuum to a

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constant weight. Moisture was determined in triplicate, and the results were averaged.

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Near-infrared reflectance spectra measurement

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NIR diffuse reflectance spectra were measured on single whole raw almond kernels using

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an extended MicroNIR 2200 spectrometer (JDSU, USA). The spectral range was collected from

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1125 – 2153 nm using sampling intervals of 8 nm per pixel. The detector used was a 128 pixel

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uncooled element InGaAs (JDSU, USA). Reﬂectance spectra data (R) were converted to

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absorbance using the log (1/R) transformation. A Spectralon® SRM-99 Diffuse Reflectance

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Standard (Labsphere®, New Hampshire, USA) was used as white calibration reference. For each

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spectrum, 1000 scans with an integration time of 550 µs were averaged.

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Data Pre-processing


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NIR spectra are complex with broad overlapping NIR absorption bands making it often

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difficult to identify unique spectral features related to individual chemical components within a

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given sample. Therefore, a mathematical treatment (pre-processing) of NIR spectra is often used

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to correct for unwanted systematic sample-to-sample variation (e.g., kernel shape, roughness of

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kernel surface); help remove spectral baseline shift and scattering caused by particle size

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differences; reduce band overlapping; and enhance spectral differences.22 Data pre-processing

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results in relevant NIR spectral data extraction without losing information while removing

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unwanted information (e.g., interferences or noise).22

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After the acquisition, NIR spectra were converted to absorbance and pre-processed using

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either standard normal variate (SNV) or a 9- point second order Savitzky-Golay filter (second

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derivative preprocessing). These two techniques alone and a combination were compared to

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determine their effectiveness at removing baseline offsets. Data pre-processing was performed

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using R and R-Studio (version 0.98.1102). The following packages were used for preprocessing

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and PLS-DA: ChemometricsWithR23, signal24, plyr25, dplyr26 and caret27.

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Determination of CD by colorimetry

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After NIR spectra were acquired, the almonds were roasted at 120°C for 90 min in a

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convection oven (Thermo Scientific, USA). Almonds were then split in half along the natural

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seam and the color of the internal kernel was measured using a ColorFlex colorimeter

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(HunterLab, USA) according to methods established previously8.

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(lightness), C (Chroma), and h (hue), according to the CIE LCh color scale were recorded using

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a portsize of 0.4 inches with D65 optical sensor, 0° geometry and 10° angle of vision. Almonds


The color values L*


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with CD (L* ≤ 71) were identified and grouped separately from those with no concealed damage

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(NCD; L* > 71).8

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Prediction of almonds with concealed damage

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Pre-processed spectra (SNV, second derivative and a combination of both) were analyzed

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using partial least squares discriminant analysis (PLS-DA). Almonds were separated into two

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groups (NCD and CD) using colorimetry. The data set (855 almonds) was then randomly divided

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into a calibration (655 almonds) and a validation (200 almonds) sample set. NCD and CD

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almonds were assigned constant values of 0 and 1 for a two-class model, respectively.

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For the calibration model, repeated cross-validation was used to find the best model.

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Calibration models were evaluated based on the percentage false positive (% fp), percentage

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false negative (% fn) and percentage error rate (% ER). A fn was defined as the percentage of

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NCD almonds classified as those with CD, while fp was defined as CD almonds classified as

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NCD. The % ER represents the percentage of total almonds incorrectly classiﬁed by the

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predictive method.4, 12

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Results and Discussion

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A representative NIR spectrum (1125-2153 nm) of the NCD almonds, after SNV pre-

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processing, is shown in Fig. 2. The spectrum is characterized by broad and unresolved absorption

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bands and is similar to spectra for peanut28, walnut10, macadamiat7, and shea nut.18 To enhance


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spectral features and compensate for baseline offsets, a second-derivative of the absorbance data,

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with respect to wavelength, was calculated. In the second-derivative data, absorbance maxima

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are converted to minima (Fig. 3). The NIR spectra obtained after applying the second-derivative

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were characterized by 10 absorption bands. These bands correlate with the major constituents of

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raw almonds: lipid (50%), carbohydrates (∼22%), and protein (∼21%).29 The absorption bands

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between 1165-1238 nm, 1692-1740 nm and 2064-2104 nm are associated with lipids. These

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include the C-H (-CH) second overtone stretching band (1200-1214 nm)30, the C-H (-CH2) first

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overtone stretching band (1700-1724 nm)6,

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absorption bands in between 1408-1462 nm and 1902-1959 nm are associated with the H-OH

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second overtone of water18 as well with protein. The absorption bands between 1692-1740 nm

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and 2064-2104 nm are associated with the absorption of protein (∼1700–1850 nm) and amino

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acids (∼2080 nm) respectively32 and the region between 1902-1959 nm correlate to water and

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amides (∼1910–1920 nm).32 Additionally, the absorption band between 2064-2104 nm can be

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associated with the O-H and the carboxylic group (C=O-O) band of carbohydrates.31

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and C-H combination band (~2098 nm).32 The

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An overlay of the averaged second derivative spectra for almonds classified as NCD and

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CD is also given in Fig. 3. The main differences between the NCD and CD spectra occur at

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1432, 1457, 1505, 1513, 1708, 1918 and 2080, 2096 nm. The absorption bands at 1432, 1457,

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1505, 1513, and 1918 nm correspond to protein33, the band at 1708 nm corresponds to free fatty

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acids and oil12, 33, and absorption at 2080 and 2096 nm correspond to carbohydrates. Almonds

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with CD present less absorbance in these regions indicating that kernels display decreased levels

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of lipids, protein and carbohydrates as compared to controls. These results corresponds to

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observations of King et al.34 (1983) who reported that almonds with CD have lower crude fat

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(oil) and total carbohydrates as compared to almonds with no concealed damage. Additionally,



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we recently demonstrated higher levels of volatiles related to lipid oxidation and amino acid

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degradation in almonds with CD as compared to almonds with no concealed damage.8 Taken

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together these results indicate the metabolic processes that activate the degradation of proteins,

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carbohydrates and lipids are involved in the development of CD. The free amino acids, sugars

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and products from the oxidation of lipids would be substrates for the Maillard reaction and

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supports the hypothesis that the Maillard reaction is involved in the formation of CD in almonds.

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Initially, multiple PLS-DA models were evaluated using the full wavelength region from

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1125-2153 nm after data pre-processing (SNV, second derivative, and SNV and the second

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derivative). In general, the best predictive models give low percentage error rates (i.e. the highest

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percentage of correct classification). Herein we found that using the full wavelength region

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resulted in models with high percentage error rates and therefore PLS-DA models were

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developed using only relevant portions of the NIR spectra. Table 1 summarize the prediction

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performance of the calibration models and validation models, which were selected as they had

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the lowest percent of error (% ER), false positive (% fp) and false negative (% fn) rates. A large

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data set (200 samples) was analyzed to optimize the prediction models. The lowest % ER (8.2%)

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was obtained using only second derivative pre-processing as compared to 9.2% when using SNV

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pre-processing, and 8.2% when using SNV and second derivative pre-processing. Although the

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% fp rate was higher for this model (17.2%) as compared to the SNV (12.6%) and SNV and

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second derivative pre-processing (10.6%), the % fn was significantly lower (12.4%) as compared

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to these models (16%).

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Previous studies employing IR to build models to discriminate differences between CD

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and NCD focused on the absorbance range between 700-1400 nm5 and on selected wavelengths

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within the 700-1400 nm absorbance range.20

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Comparisons of these results with results


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obtained herein are summarized in Table 2. Although the % fp were lower across these studies

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(0.7-5.4%), as compared with our results (12.4 – 16.1%), the rate of % fn were significantly

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higher (11.1-62.96%) than those obtained using our predictive models (10.6 – 17.2%).

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Additionally, the % ER ranged from 5.8–27.5% whereas our predictive models gave a much

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narrower range of 8.2 – 9.2%. The three PLS-DA models presented herein offer significant

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improvements in the prediction capabilities and are able to identify almonds with CD with 90.8 –

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91.8% certainty based upon calibration models. Although any of the three models presented

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could be considered for further development of a rapid in-line screening method for detecting

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CD in raw almonds, the PLS-DA model based on the second derivative spectra and utilizing four

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wavelength ranges (i.e.1408–1462, 1692–1740, 1902–1959 and 2064-2104 nm) gives the lowest

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rate of false negatives and may be the best choice for further method development.

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Our results indicate that these PLS-DA predictive models offer advantages over

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previously reported models and that CD is related to the degradation of lipids, carbohydrates and

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proteins in almonds.

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Fassio, A.; Cozzolino, D. Non-destructive prediction of chemical composition in

Workman Jr, J.; Weyer, L., Appendix D. Spectra - Structure Correlations for Near



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(34)

Jr, A. D. K.; Halbrook, W. U.; Fuller, G.; Whitehand, L. C. Almond Nutmeat Moisture

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and Water Activity and its Influence on Fungal Flora and Seed Composition. J. Food Sci. 1983,

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48, 615-617.

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Abbreviations Used

334

Concealed damage, CD; No concealed damage, NCD; NIR, Near Infrared Spectroscopy;

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SNV, Standard Normal Variate; PLS, Partial Least Square; DA, Discriminant Analysis; fp, False

336

Positive; fn, False Negative; % ER., percentage error rate.

337

338

Funding Sources

339

The Almond Board of California provided financial support for this study. We would also

340

like to acknowledge the support of the John Kinsella Endowed Chair in Food, Nutrition and

341

Health.

342

343

Acknowledgements

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The authors thank Franz Niederholzer (UC Farm Advisor Colusa/Sutter/Yuba Counties)

345

for many thoughtful conversations on almond flavor and breeding and for providing almond

346

samples.

347



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FIGURE LEGENDS:

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350

Figure 1.

5% moisture (control) and 11% moisture (concealed damage).

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352

Figure 2.

The mean standard normal variate (SNV) pre-processed absorbance spectra of almonds with no concealed damage (NCD).

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354

Color development in raw and roasted almonds (120°C for 90 min) exposed to

Figure 3.

A comparison of the mean second derivative (Savitzky-Golay, 9-smoothing

355

points) pre-processed spectra of almonds with no concealed damage (NCD) and

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concealed damage (CD).

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Figure 1

368 369



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Figure 2

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2

Absorbance (log 1/R)

1.5 1 0.5 0 1120 -0.5

1220

1320

1420

1520

1620

1720

1820

-1 -1.5 -2 -2.5

Wavelength (nm)

372 373

374

375

376

377

378


1920

2020

2120

Page 21 of 24

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Figure 3

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0.004 0.003

Second derivative

0.002 0.001 0 1120 -0.001

1220

1320

1420

1520

1620

1720

1820

2020

2120

NCD CD

-0.002 -0.003 -0.004 -0.005

1920

Wavelength (nm)

381 382



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Table 1. Results of the PLS-DA model using standard normal variate (SNV), second derivative,

385

and SNV and second derivative pre-processing.

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a) Calibration Model SNV 1408 – 1465 1902 – 1959

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Second derivative Wavelength selected 1408 – 1465 (nm) 1692 – 1740 1902 – 1959 2064 – 2104 # Latent variables 4 7 ROC* / % Error rate 0.908/9.2 0.918/8.2 Specificity / % fn 0.839/16.1 0.876/12.4 Selectivity / % fp 0.874/12.6 0.828/17.2 * Area under the Receiver Operating Characteristics

Second derivative + SNV 1408 – 1465 1692 – 1740 1902 – 1959 4 0.918/8.2 0.840/16.0 0.894/10.6

b) Validation Model

% Error rate % false positive (% fp) % false negative (% fn)

SNV 9 8 11

Second derivative 7 8 6

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Second derivative + SNV 9 9 7

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Table 2.

of almond with concealed damage (CD).

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Range (nm)

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Comparison of NIR validation results between methods used in the classification

Pearson, T.C (1998)

Nakariyakul, S. (2014)

Nakariyakul et al. (2011)

700 – 1400

700 – 1400 (selected wavelength) 5.8 2.91 – 3.41 14.81 – 62.96

700 –1400 (selected wavelength) 8.8 0.58 – 1.74 31.48 – 53.70

% Error rate 12.4 – 27.5 % False positive 0.7 – 5.4 % False negative 11.1 – 23.8 * Based on calibration models

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398 399 400 401 402 403 404 405 406 407 408 409 410


Results Obtained Herein* 1125–2153 (selected wavelength) 8.2 – 9.2 12.4 – 16.1 10.6 – 17.2


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Table of Contents Graphics

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Use of Near-Infrared Spectroscopy and ... - ACS Publications

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