Rapid Prediction of Camelina Seed Oil Content Using Near-Infrared

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Rapid prediction of oil content of camelina seeds using near-infrared spectroscopy Ke Zhang, Zhenglin Tan, Chengci Chen, Xiuzhi Susan Sun, and Donghai Wang Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.6b02762 • Publication Date (Web): 04 Apr 2017 Downloaded from http://pubs.acs.org on April 7, 2017

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Energy & Fuels

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Rapid prediction of camelina seeds oil content using near-infrared

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spectroscopy

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Ke Zhanga,#, Zhenglin Tana,b,#, Chengci Chenc, Xiuzhi Susan Sund, and Donghai

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Wanga*

5 a

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Department of Biological and Agricultural Engineering, Kansas State University, Manhattan,

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KS 66506, USA b

College of Tour and Hotel Management, HuBei University of Economics, Wuhan, 430205, P.R.

9 10 11

China c

Central Agricultural Research Center, Montana State University, Moccasin, MT 59462, USA d

Department of Grain Science and Industry, Kansas State University, Manhattan, KS 66506,

12 13 14

USA #

The first two authors contributed equally to this work

*Corresponding author. Telephone: 785-5322919, Fax: 785-5325825. E-mail: [email protected].

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Abstract Camelina is a promising feedstock due to its ability to provide high quality edible oil and jet

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fuel. However, predicting its oil content currently requires time and labor intensive analysis.

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Near-infrared (NIR) spectroscopy provides a rapid, low-cost determination approach for oil seed

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characterization. The objective of this study was to develop NIR model to predict camelina oil

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content using 200 camelina seeds simples. Partial least squares regression (PLS) and principal

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component regression (PCR) were used to compare the performance of calibration models with

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full spectra range (4,000-1,000 cm-1). PLS regression showed better prediction performance than

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PCR. The optimal model provided excellent fitness with an R2 of 0.94 and root mean square of

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prediction error of 0.495%; madding the model useful in various applications, including quality

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assurance and screening. This study confirmed that the NIR method significantly reduces time

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(from 60 to 1 min) and cost (from 20 to 1 USD) required to determine camelina seed oil content.

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Last but not least, this study leverage a high-throughput, cost-effective prediction method of

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camelina oil content to facilitate plant breeding and genetics studies. Future work on the NIR

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model should focus on developing a model system to achieve rapid analysis of genomics at low

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cost to assist plant feedstock improvement.

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Keywords: Camelina; oil content; near-infrared; chemometric analysis; prediction model

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1. Introduction

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Camelina sativa (L), commonly as false flax, is an underutilized oil seed crop belonging to the

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Brassicaceae family. Camelina oil is a promising candidate for high quality edible oils because it

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contains up to 90% unsaturated fatty acid with high α-linolenic acid (18:3, omega-3) content 1. It

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also reduces cholesterol and provides resistance to stress 2. Recent studies have shown that

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camelina is a superior feedstock for biodiesel and jet fuel, specifically in fuel performance.

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Commercial airline and military fighter jet testing showed that jet fuel made from camelina oil

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has up to 80% reduction of greenhouse gas emissions compared to petroleum-based jet fuel 3.

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Camelina also offers many agronomic advantages over traditional commodity oilseed crops, such

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as low requirement for water, fertilizer, and lands; good tolerance to adverse environmental

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conditions, and high resistance to alternaria black spot and other diseases and pests 4, 5. In general,

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camelina contains 29.9% to 38.3% oil, 23% to 30% protein, 10% carbohydrates, and 6.6% ash

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based on % w/w, depending on variations in breeding conditions 6. Many camelina breeding

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programs have been established in order to improve camelina characteristics such as oil content,

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fatty acid composition, seed size, disease resistance, and yield7-10. These programs require large

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numbers of camelina seeds to be rapidly and cost-effectively screened for multiple traits, but the

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traditional wet chemical method to measure oil content is time-consuming. Near-infrared (NIR)

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spectroscopy, by contrast, provides a reliable and efficient prediction tool for food,

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pharmaceutical, and agricultural applications11-13.

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NIR is a fast approach based on spectrum absorption by molecular overtone and combination

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vibrations within a sample. NIR offers some advantages such as high-throughput, less-

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preparation, and non-destructive prediction and can be used in harsh industrial settings 14, 15.

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Previous research has shown that NIR is an effective method for motor oil classification in terms 3 ACS Paragon Plus Environment

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of base stock and viscosity-based classes 16. Azizian and Kramer 17 developed several useful FT-

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NIR models in order to classify 55 oil, fat, and oil/fat mixtures. In addition, NIR discriminate

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analysis has been conducted on edible oils 18, and many oil parameters (acidity and peroxide

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index) have been analyzed using NIR 19. Wang et al. 20 used NIR to discriminate soybean oil

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adulteration in camelina oils with an R2 of 0.992 and root mean standard error of cross validation

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(RMSECV) of 1.79 for a PLS model. Weinstock et al. 21 built an NIR model that predicted oil

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and oleic acid concentrations in individual corn kernels with a root mean square error of

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prediction (RMSEP) of 0.7% and 14%, respectively. NIR has also been successfully employed to

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model the oil contents of peanut 22, soybean seed 23, fish oil 24, and ground nuts 25.

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However, no published information exists on modeling the oil content of camelina seed using

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NIR. The objective of this research was to develop NIR models coupled with chemometrics in

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order to rapidly and cost-effectively predict camelina oil content.

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2. Materials and Methods

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2.1Materials

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The study used two hundred camelina whole seed samples from different fertility trials, crop

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rotation, and nitrogen treatment studies, provided by the Central Agricultural Research Center,

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Montana State University, Bozeman, Montana, United States. These camelina seed samples

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consisted of four cultivars planted in 2013 in unique environments in Montana with various

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fertility levels, resulting in large variations in oil contents.

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2.2 Camelina oil content


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Camelina seeds were grinded using Micro-Mill grinder (SP Scienceware, NJ, USA) to < 0.5

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mm powder and encapsulated in a plastic bag in a desiccator to homogenize moisture content.

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Oil content measurement of camelina seeds was based on AOAC 2003.5 26. In briefly, a 2.0 g

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grinded camelina seed sample was weighed in a single thickness cotton cellulose thimble (80

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mm external length by 33 mm internal diameter) and extracted in a Soxtec HT2 apparatus

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comprised of a 1045 Extraction Unit 1046 Service Unit (Tecator, Hoganas, Sweden) with 60 ml

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of hexane (boiling period: 20 min; rinsing period: 40 min). When almost all the hexane was

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collected, the extraction cup was dried for 30 min at 103 ℃. The cup was cooled to room

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temperature and weighed. Extracted oil was collected in a 2-ml centrifuge tube and stored at 7 ℃

for further use. Oil content is calculated as: =

!"#$ %&' '(&) *)"+&* ,*"-. − !"#$ %&' ,*"-. 0!1'(* ,*"-.

× 344 (3)

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2.3 NIR spectra

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An Antaris II FT-NIR analyzer (Thermo Scientific Incorporated, Madison, WI, U.S.) was

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applied to collect NIR spectra in reflectance mode. Intact camelina seeds were measured using a

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sample cup spinner combined with an integrating sphere to rapidly and accurately obtain bulk

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information by spinning the sample cup. Each spectrum was averaged with 32 accumulations at a

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resolution of 4 cm-1 in the wavelength range of 4,000–10,000 cm-1.

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2.4 Spectra treatment and chemometrics

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TQ Analyst 8.6.12 (Thermo Scientific Incorporated, Madison, WI, U.S.) was used to pretreat

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and analyze the spectra. Path length was calibrated using standard normal variate (SNV), which 5 ACS Paragon Plus Environment

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has a mean of 0, a standard deviation of 1, and multiplicative signal correction (MSC). MSC is

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another spectrum processing method by regressing a measured spectrum against a reference

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spectrum and then corrects the measured spectrum using the slope. The Savitzky-Golay (SG)

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filter was applied to a set of digital data points to increase the signal-to-noise ratio and reducing

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random noise. First and second derivatives were used as a treatment method to resolve spectra

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peak overlap and eliminate linear baseline drift. The first derivative was the rate of change of

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absorbance with respect to wavelength, whereas the second derivative corresponded to the

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curvature or concavity of the graph. First and second spectra formats were compared based on

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RMSEP, R2 and RPD of models.

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In order to eliminate bias in the subsets and carry out a calibration subset and prediction subset

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with a ratio of 3 to 1, all of 200 camelina seed samples was arranged as descending order based

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on measured value. One in every four spectra was randomly selected to the prediction subset (50),

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with remaining spectra as calibration samples (150). Both full spectra range (4,000 to 10,000 cm-

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1

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PCR methods described in the previous publications 27, 28 were applied for models building. The

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performance models were evaluated in terms of R2, RMSEP, and ratio of standard deviation of

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calculated subset (SDy) to RMSEP (RPD), calculated with the following equations:

) and reduced spectra range (7500-7000, 5600 – 5000, 4700-4250 cm-1) were used. PLS and

789:; = < 7;I =

B

C ∑?DE (ŷ? − @? )A

(2)

FG

9IJ 789:;

(3)


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where np is the number of samples in the prediction subset; yi and ŷi are the measured value and

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predicted value of the i th sample, respectively.

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The Chauvenet test was applied to remove outliers defined as points at distances greater than

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3.0 in the principal component (PC) space. Predicted residual error sum of squares (PRESS)

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diagnostic function was used to determine the number of factors necessary for calibration.

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3. Results and Discussion

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3.1 Sample statistics

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The camelina seed samples’ oil content range mean, and standard deviation as measured using

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reference methods, are summarized in Table 1. The results showed good range and distribution:

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25.9 to 37.7% oil content with a mean of 32.3% and standard deviation of 2.1% for the full set

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and a mean of 32.2% and standard deviation of 2.09% for the calibration subset. The range,

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mean, and standard deviation of the calibration subset covered those of the prediction subset,

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which was from 28.3 to 36.6% with a mean of 32.5% and standard deviation of 2.0%. The oil

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content range was consistent with published informaton1, 6. In addition, both subsets had similar

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and consistent distribution patterns for prosperous model development.

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3.2 Samples spectra

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NIR spectra of 200 camelina seed samples with the full range (4000-10000 cm-1) are

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demonstrated in Fig. 1. In general, absorption peaks were observed at 8250, 6800, 5830, 5765

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cm-1, with several small peaks between 4800 and 4550 cm-1. The first peak around 8250 cm-1

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was related to the second overtones of CH stretching vibration 24. The second peak was relatively

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large, from 7200 to 6600 cm-1, resulting from the OH stretch overtone 29. Peaks at 5830 and 5765


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cm-1 corresponded to the first overtones of CH stretching vibrations of –CH3, –CH2 and –

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HC=CH– 30. Small peaks between 4800 and 4550 cm-1 were attributed to the C=C and C–H

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stretch combination tone of cis unsaturated fatty acids (C18:1 and C18:2) 24.

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3.3 NIR model development for camelina oil content

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Principal component analysis (PCA), employed prior to NIR model development, derived 10

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PCs from the spectral data in order to analyze the relevant and interpretable structure in the data.

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In addition, five outliers were identified based on the Chauvenet test. Chauvenet test found a

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probability band, which contains all the statistically acceptable data. Although several dots in Fig.

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2 have a relative distance from the cluster, these samples were not strictly considered outliers

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because they represented real variation in industrial conditions. Both PCR and PLS were used to

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build calibration models; however, PLS consistently demonstrated better performance and

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statistic results than PCR in terms of R2 and RPD. PLS also showed more quantitative analytical

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power than PCR based on R2 and RPD (Table 2).

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The PCA score showed how each spectrum in the PLS was represented by each PC during

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model calibration. Each PC presented an independent factor from spectral variation in the data.

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Fig. 2 shows plots of PC analysis scores (PC1 versus PC2) of camelina oil content. PC1

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accounted for 98.73% of the variation explanation and described most variation in the calibration

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spectra. PC2 represented 1.06% of the variation in the spectra. The first two PCs accumulatively

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accounted for more than 99% of the variation among samples. The pattern of PC score dots

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seemed reasonably random, suggesting good representation for all camelina seed samples. In

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order to determine sufficient amounts of variation in the data and a useful spectral region, PC1

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loading was analyzed, as shown in Fig. 3. Orthogonal loading spectra showed strong loadings in

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PC1 from 7500 to 7000 cm-1 and from 5600 to 5000 cm-1. These spectral regions corresponded 8 ACS Paragon Plus Environment

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to the first overtone of OH and the CH2 overtone, both of which were representative absorption

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of fatty acid in oil 29.

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Spectra processes including SNV, MSC, SG, and derivatives were applied to optimize the

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models. The optimal model was developed by comparing the performance of every combination

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of derivative treatments, spectra processes, and quantitative methods 31. SNV enhanced the

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performance of prediction compared to MSC and SG, suggesting that SNV resolved the

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accentuation problems caused by the light scattering of a slightly uniform camelina seeds pile.

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First and second derivatives were used to further improve the model, and their performances are

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summarized in Table 2. First derivative significantly enhance the performance of calibration and

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prediction models, but no improvement was found for the second derivative, possibly because

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the second derivative brought more false information from mathematical artifacts when

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identifying smaller absorption signals 32. A reduced spectral range was also applied in order to

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enhance the goodness-of-fit of models.

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In Fig. 4, the mean spectrum (red line) shows the average of 200 camelina seed spectra. The

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variance spectrum (blue line), which shows variance in all spectra, was calculated from the

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square root of the spectral variance for each oil content across all spectra. The mean and variance

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spectra of 200 camelina seed samples revealed a similar trend, suggesting that the calibration

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model could be robust. The regression coefficient line as function of wavelength (black line)

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indicates wavelengths with large weights in the calibration model. In order to improve the model,

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three wavelength ranges (7500-7000, 5600-5000, 4700-4250 cm-1) were selected resulting from

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their high coefficient and high variance, which indicate high correlation and large variation. The

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reduced range demonstrated better goodness-of-fit for both calibration and prediction models

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than the full range models, with an R2 of 0.97, RMSEP of 0.495%, and RPD of 4 for the


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prediction sample subset, suggesting that this model provides an accurate prediction and could be

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used in most applications 33. The reduced wavelength region correlated best with samples’ oil

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content with reduced random noise. Previous studies have also reported that similar wavelength

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regions highly corresponded to the oil instauration index and cis/trans ratio of unsaturated fatty

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acids 34, 35.

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PRESS function was used to avoid overfitting the models. In Fig. 5, the R2 value increased

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sharply at three factors, peaked gradually at five factors, and then leveled off to a constant value.

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Fig. 6 plots chemical reference data against NIR-predicted values in the calibration and

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prediction subsets of the final model. This plot illustrates the relationship between the chemical

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data and NIR calibration models as well as the presence of outlier samples, suggesting that the

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optimal model was prosperous and did the terrific job on the prediction of camelina seed oil

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content. The conventional method to measure the oil content of oil seeds requires grinding the

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sample and extracting oil using chemical solvent, which is time consuming and labor intensive.

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Our NIR method, by scanning whole oil seeds using spectroscopy, significantly reduced the time

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(from an estimated 60 to 1 min) and cost (from an estimated 20 to 1 USD)to determine camelina

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oil content.

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4. Conclusion

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This study successfully developed models to predict camelina seeds’ oil content. The optimal

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model was achieved by PLS quantitative analysis, SNV, and first derivative with a prediction R2

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of 0.94, RMSEP of 0.495%, and RPD of 4. NIR models could improve the speed of oil content

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determination and can assist in crop management with increased efficiency.

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Acknowledgment 10 ACS Paragon Plus Environment

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This work was supported by Biomass Research and Development Initiative Program with grant

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number of 2012-10006-20230 from the U.S. Department of Agriculture National Institute of

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Food and Agriculture.

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Table 1. Oil content of 200 camelina seed samples measured using reference method.

211 Full set

Oil content (%)

Calibration subset

Prediction subset

Range

Mean

SD

Range

Mean

SD

Range

Mean

SD

25.9-37.7

32.3

2.1

25.9-37.7

32.2

2.1

28.3-36.6

32.5

2.0

212 213 214

Table 2. NIR calibration and prediction statistics for camelina oil content prediction. Full range (10000 - 4000cm-1)

Reduced range (7500-7000, 5600 – 5000, 4700-4250 cm-1)

Raw spectra

First derivative

Second derivative

Raw spectra

First derivative

Second derivative

0.83 0.80 0.886 2.23 2

0.89 0.88 0.668 2.96 4

0.79 0.67 1.19 1.66 1

0.93 0.91 0.606 3.27 5

0.97 0.94 0.495 4 5

0.87 0.81 0.952 2.08 4

0.79 0.67 1.21 1.63 2

0.78 0.68 1.17 1.69 10

0.67 0.67 1.15 1.72 2

0.81 0.85 0.732 2.71 2

0.83 0.88 0.666 2.97 2

0.77 0.74 0.974 2.03 2

PLS R2, cal. R2,val. RMSEP RPD Factor used

PCR R2, cal. R2,val. RMSEP RPD PC used

215 216


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Fig. 1. NIR spectra of 200 camelina seed samples.

219 220 221


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0.4

0.2

PC2 Score x 10 (1.06%)

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0.0

-0.2

-0.4

-0.6

-0.8 -0.10

-0.05

0.00

0.05

0.10

PC1 Score (98.73%) 222 223

Fig. 2. Principal component analysis scores of camelina seed sample oil content for PLS model.

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0.4

PC1 loading 0.3

0.2

Absorbance

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0.1

0.0

-0.1

-0.2

-0.3 10000

9000

8000

7000

6000

5000

4000

-1

Wavenumbers (cm ) 225 226

Fig. 3. Loading for the first principal component of camelina seeds sample oil content.

227 228


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0.74

Regression coefficient spectra Mean spectra Variance spectra

0.72

1.4

0.14

1.2

0.12

1.0

0.70

0.8 0.68

Absorbance

Regression coefficient

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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0.10

0.08

0.6 0.66

0.06 0.4

0.64

10000

0.04 9000

8000

7000

6000

5000

0.2 4000

-1

229 230

Wavenumbers (cm ) Fig. 4. Regression coefficient, mean, and variance spectra of oil content in PLS model.

231 232


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PRESS R2

1.0 500 0.9

400

0.8

0.7 300

R2

PRESS

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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0.6 200 0.5 100

0.4 0

2

4

6

8

10

Factor 233 234

Fig. 5. PRESS and R2 vs. calculated factors in the prediction model.

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Calculation Prediction

38

36

Calculated value (%)

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34

32

30

28

26 26

28

30

32

34

36

38

Measured value (%) 237 238 239 240

Fig. 6. Comparison of calculated versus measured camelina seed sample oil contents.

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Rapid Prediction of Camelina Seed Oil Content Using Near-Infrared

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