scholarly journals Pendugaan Kadar Air Beras Ketan Putih Dengan Teknologi NIRS Menggunakan Metode Principal Component Regression (pretreatment De-Trending, Derivative-2, dan Standart Normal Variate)

2020 ◽  
Vol 4 (4) ◽  
pp. 502-511
Author(s):  
Mardiantono Mardiantono ◽  
Fachruddin Fachruddin ◽  
Zulfahrizal Zulfahrizal
Keyword(s):  
Water Content ◽  
Model Performance ◽  
Principal Component Regression ◽  
Principal Component ◽  
Standard Normal Variate ◽  
Glutinous Rice ◽  
Standard Normal ◽  
Ft Ir ◽  
Pcr Method ◽  
Rice Water

Abtrak. Kadar Air merupakan salah satu komponen penting dalam beras ketan putih yang dapat mempengaruhi kualitas dari beras ketan putih. Penelitian ini bertujuan menguji dan mengevaluasi teknologi NIRS sebagai metode cepat dan tepat dalam memprediksi kadar air beras ketan dengan metode Principal Component Regression (PCR) serta menentukan metode koreksi spektrum yang terbaik dan akurat untuk memprediksi kadar air beras ketan dengan menggunakan pretreatment De- Trending, Derivative-2, dan Standart Normal Variate (SNV). Penelitian ini menggunakan beras ketan putih yang didapat dari pasar Rukoh Banda Aceh, yang berjumlah 35 sampel. Perlakuan yang diberikan adalah tanpa perendaman, dibasahi, dan perendaman selama 5, 10, 15, 20, dan 25 menit. Prediksi kadar air beras ketan dengan NIRS menggunakan alat self developed FT-IR IPTEK T-1516 dan metode referensi yang digunakan adalah metode gravimetri yang berdasarkan pada Association of Official Analytical Chemists (AOAC). Pengolahan data menggunakan Unsclambers sofware® X version 10.5. Hasil penelitian menunjukkan bahwa NIRS dengan metode PCR mampu menghasilkan model yang baik untuk pendugaan beras ketan. Penelitian ini menghasilkan empat model pendugaan kadar air beras ketan dimana satu model tergolong very good performance (RPD3) dan tiga model tergolong good model performance (RPD2) sehingga dapat dikatakan bahwa semua model yang dihasilkan layak dan baik untuk pendugaan kadar air beras ketan. Pretreatment terbaik pada penelitian ini adalah Standart Normal Variate (SNV) dengan nilai RPD 3,12, r sebesar 0,95, R2 sebesar 0,89, dan RMSEC sebesar 2,34.Estimation of White Gluttony Rice Rate With NIRS Technology Using Principal Component Regression Method (Pretreatment De-Trending, Derivative-2, dan Standart Normal Variate)Abstract. Water content is one important component in white glutinous rice which can affect the quality of white glutinous rice. This study aims to test and evaluate NIRS technology as a fast and precise method for predicting glutinous rice water content with the Principal Component Regression (PCR) method and determine the best and accurate spectrum correction method for predicting glutinous rice water content using the De-Trending, Derivative pretreatment -2, and Standard Normal Variate (SNV). This study uses white sticky rice obtained from the Rukoh market in Banda Aceh, which amounted to 35 samples. The treatment given is without soaking, soaking, and soaking for 5, 10, 15, 20, and 25 minutes. The prediction of glutinous rice moisture content with NIRS uses a self-developed FT-IR IPTEK T-1516 tool and the reference method used is the gravimetric method based on the Association of Official Analytical Chemists (AOAC). Data processing using Unsclambers software X version 10.5. The results showed that NIRS with the PCR method was able to produce a good model for estimating glutinous rice. This study produced four models of estimation of glutinous rice water content where one model was classified as very good performance (RPD 3) and three models were classified as good model performance (RPD 2) so that it could be said that all the models produced were suitable and good for estimating rice water content sticky rice. The best pretreatment in this study is the Standard Normal Variate (SNV) with an RPD value of 3.12, r of 0.95, R2 of 0.89, and RMSEC of 2.34. 

scholarly journals Prediksi Kadar Air Beras Menggunakan NIRS dengan Metode PLS dan Pretreatment Standard Normal Variate, Derivative-I, Savitzky Golay Smoothing

2019 ◽  
Vol 4 (1) ◽  
pp. 628-637
Author(s):  
Nurhasanah Nurhasanah ◽  
Kiman Siregar ◽  
Zulfahrizal Zulfahrizal
Keyword(s):  
Moisture Content ◽  
Least Squares ◽  
Water Content ◽  
Partial Least Squares ◽  
Correction Method ◽  
Standard Normal Variate ◽  
Standard Normal ◽  
Ft Ir ◽  
Rice Water ◽  
Pls Method

Abstrak. Kadar air merupakan suatu komponen penting dalam beras. Pengukuran kadar air dapat dilakukan menggunakan oven, alat elektronik seperti moisture tester, serta dengan penggunaan gelombang elektromagnetik seperti NIRS. Penelitian ini bertujuan menguji dan mengevaluasi teknologi NIRS sebagai metode cepat dan tepat dalam memprediksi kadar air beras dengan metode Partial Least Squares (PLS) serta menentukan metode koreksi spektrum yang terbaik dan akurat untuk memprediksi kadar air beras dengan menggunakan pretreatment Standard Normal Variate (SNV), Derivative- I (D-1)danSavitzky Golay Smoothing (SGS). Penelitian ini menggunakan Beras merk MB yang berasal dari pasar Rukoh Banda Aceh, yang berjumlah 20 sampel atau 300 gram. Perlakuan yang diberikan pada beras yaitu tanpa perendaman dan perendaman selama 5, 10, dan 15 menit. Prediksi kadar air beras dengan NIRS menggunakan alat self developed FT-IR IPTEK T-1516 dan metode referensi yang digunakan adalah metode gravimetri yang berdasarkan pada Association of Official Analytical Chemists (AOAC). Pengolahan data menggunakan Unscramble software® X version 10.5. Hasil penelitian menunjukkan prediksi kadar air beras dengan metode Partial Least Squares (PLS) menghasilkan good model performance dengan nilai RPD yang didapat yaitu 2,24 dan metode koreksi terbaik pada penelitian ini adalah Derivative-I dengan nilai RPD 2,57, r sebesar 0,9169, R2 sebesar 0,8407 dan RMSEC sebesar 1,6620.Prediction of Rice Moisture Content Using NIRS with PLS and Pretreatment Standard Normal Variate, Derivative-I, Savitsky Golay SmoothingAbstract. Moisture content is an important component of rice. Measurement of moisture content can be analyzed using an oven, electronic devices such as moisture tester, and by using the use of electromagnetic waves such as NIRS. This study aims to examine and evaluate NIRS technology as a faster and proper method in predicting rice moisture content by Partial Least Squares (PLS) method and determining the best and accurate spectrum correction method to predict rice water content using Standard Normal Variate (SNV) pretreatment, Derivative-I (D-1) and Savitzky Golay Smoothing (SGS). This study uses MB brand rice from the Rukoh market in Banda Aceh, with a total of 20 samples or 300 grams. The treatment given to rice is without soaking and soaking for 5, 10, and 15 minutes. Prediction of rice water content with NIRS using a self-developed FT-IR IPTEK T-1516 and the reference method used is a gravimetric method based on the Association of Official Analytical Chemists (AOAC). Data processing using Unscramble software® X version 10.5. The results showed the prediction of rice water content by the Partial Least Squares (PLS) method showed a good performance model with the RPD value obtained was 2.24 and the best correction method in this study was Derivative-I with an RPD value of 2.57, r of 0, 9169, R2 of 0.8407 and RMSEC of 1.6620.

scholarly journals Transformasi Attenuated Total Reflectance Untuk Prediksi Vitamin C Pada Buah Mangga Arumanis (Mangifera indica)

2019 ◽  
Vol 4 (3) ◽  
pp. 75-84
Author(s):  
Muslem Muslem ◽  
Sri Purnama Sari ◽  
Agus Arip Munawar
Keyword(s):  
Principal Component Analysis ◽  
Vitamin C ◽  
Near Infrared ◽  
Mangifera Indica ◽  
Principal Component Regression ◽  
Principal Component ◽  
Attenuated Total Reflectance ◽  
Total Reflectance ◽  
Ft Ir ◽  
Pcr Method

Abstrak, Parameter yang digunakan dalam penilaian mutu buah mangga antara lain ukuran atau berat, kekerasan, tingkat ketuaan serta bebas dari cacat. Kekerasan pada buah mangga merupakan fungsi dari tingkat kematangan, sedangkan kematangan berhubungan dengan tingkat ketuaan yang dapat diduga melalui penampilan visual. Vitamin C merupakan vitamin yang larut dalam air dan esensial untuk biosintesis kolagen.pengukuran vitamin C pada buah mangga menggunkan metode tetrasi, dan penggunaan gelombang elektromaknetik seperti Near Infrared. Penelitian ini bertujuan untuk memprediksi kadar vitamin C dalam buah mangga menggunakan metode Spektrofotometri UV-Vis dan Iodimetri, serta membandingkan hasil dari kedua metode tersebut. Sampel yang diidentifikasi yaitu buah mangga yang sudah matang dengan menggunakan model transformasi Attenuated Total Reflectance dan menggunakan metode Principal Component Analysis (PCA) dan menggunakan metode Principal Component Regression  (PCR). Penelitian ini menggunakan buah mangga jenis Arumanis, yang berjumlah 30 sampel. Prediksi vitamin C dengan NIRS menggunakan alat FT-IR IPTEK T-1516. Pengolahan data menggunakan Unscramble software® X versi 10.5. Hasil penelitian menunjukkan prediksi vitamin C mangga dengan metode Principal Component Regression (PCR) menghasilkan sufficient performance dengan nilai RPD yang didapat yaitu 2,0083 (r) sebesar 0,8638 , (R2 ) sebesar 0,7463 dan (RMSEC) sebesar 5,1854 Transformation Of Attenuated Total Reflectance (ATR) Near Infrared for prediction of Vitamin C In Arumanis Mangoes (Mangifera Indica)Abstract. Parameters used in assessing the quality of mangoes are size or weight, hardness, age level and free from defects. Hardness in mangoes is a function of maturity level, while the maturity is related to the level of aging that can be predicted through visual appearance. Vitamin C is a water-soluble vitamin which is essential for collagen biosynthesis. The measurement of vitamin C in mangoes use tetration methods, and the using of electromagnetic waves such as Near Infrared. This study aims to predict vitamin C contains in mango fruit using the UV-Vis and Iodymetry Spectrophotometry method, and comparing the results of the two methods. The samples identified were mature mangoes using the attenuated total reflectance transformation model and using the Principal Component Analysis (PCA) method also using the Principal Component Regression (PCR) method. This study used Arumanis mangoes, which amounted to 30 samples. Prediction of vitamin C with NIRS using the FT-IR IPTEK T-1516. Data processing use the Unscramble software® X 10.5 version. The results showed that the prediction of vitamin C mango using the Principal Component Regression (PCR) method resulted in sufficient performance with the obtained RPD value of  2,0083, (r) of 0,8638, (R2) of 0,7463 and (RMSEC) of 5,1854.

scholarly journals Determination of Total Flavonoids for Paris Polyphylla Var. Yunnanensis in Different Geographical Origins Using UV and FT-IR Spectroscopy

2019 ◽  
Vol 102 (2) ◽  
pp. 457-464
Author(s):  
Yuangui Yang ◽  
Yanli Zhao ◽  
Zhitian Zuo ◽  
Yuanzhong Wang
Keyword(s):  
Uv Spectrophotometry ◽  
Total Flavonoids ◽  
Least Squares Regression ◽  
Standard Normal Variate ◽  
Paris Polyphylla ◽  
Orthogonal Signal Correction ◽  
Relevant Variables ◽  
Standard Normal ◽  
Ft Ir

Abstract Background: Paris polyphylla var. Yunnanensis (PPY) is used in the clinical treatment of tumors, hemorrhages, and anthelmintic. Objective: The aim of this study is to determine total flavonoids of PPY in the Yunnan and Guizhou Provinces, China. Methods: In this study, total flavonoids were determined by UV spectrophotometry at first. Then, Fourier transform mid-infrared (FT-IR) based on various pretreatments include standard normal variate (SNV), first derivative (FD), second derivative (SD), Savitzky-Golay (SG), and orthogonal signal correction (OSC) were investigated. In addition, several relevant variables were screened by competitive adaptive reweighted sampling (CARS). The contentof total flavonoids and selected variables of FT-IRwere used to establish a partial least squares regression for PPY in different regions. Results: The results indicated that CARS was an effective method for decreasing the variable of thedatabase and improving the prediction of the model.FT-IR with pretreatment SNV + OSC + FD + SG had thebest performance, with R2 > 0.9 and residual predictive deviation = 3.3515, which could be used forthe predictive model of total flavonoids. Conclusions: Those results would provide a fast and robust strategy for the determination of total flavonoids of PPY in different geographical origin. Highlights: Various pretreatments, including SNV, FD, SD, SG, and OSC, were compared; several relevant variables were selected by CARS; and the content of total flavonoids and selected variable were used to establish a partial leastsquares regression for PPY in different regions.

scholarly journals Diagnostic Potential of FT-IR Fingerprinting in Botanical Origin Evaluation of Laurus nobilis L. Essential Oil is Supported by GC-FID-MS Data

Molecules ◽  
2020 ◽  
Vol 25 (3) ◽  
pp. 583 ◽  
Author(s):  
Stella A. Ordoudi ◽  
Maria Papapostolou ◽  
Stella Kokkini ◽  
Maria Z. Tsimidou
Keyword(s):  
Model Performance ◽  
Principal Component ◽  
Cost Effective ◽  
Species Variation ◽  
Laurus Nobilis ◽  
Analytical Technique ◽  
Spectral Bands ◽  
Diagnostic Potential ◽  
Production History ◽  
Ft Ir

The last years, non-targeted fingerprinting by Fourier-transform infrared (FT-IR) spectroscopy has gained popularity as an alternative to classical gas chromatography (GC)-based methods because it may allow fast, green, non-destructive and cost-effective assessment of quality of essential oils (EOs) from single plant species. As the relevant studies for Laurus nobilis L. (bay laurel) EO are limited, the present one aimed at exploring the diagnostic potential of FT-IR fingerprinting for the identification of its botanical integrity. A reference spectroscopic dataset of 97 bay laurel EOs containing meaningful information about the intra-species variation was developed via principal component analysis (PCA). This dataset was used to train a one-class model via soft independent modelling class analogy (SIMCA). The model was challenged against commercial bay laurel and non-bay laurel EOs of non-traceable production history. Overall, the diagnostic importance of spectral bands at 3060, 1380–1360, 1150 and 1138 cm−1 was assessed using GC-FID-MS data. The findings support the introduction of FT-IR as a green analytical technique in the quality control of these often mislabeled and/or adulterated precious products. Continuous evaluation of the model performance against newly acquired authentic EOs from all producing regions is needed to ensure validity over time.

Identification Performance of Different Types of Handheld Near-Infrared (NIR) Spectrometers for the Recycling of Polymer Commodities

2018 ◽  
Vol 72 (9) ◽  
pp. 1362-1370 ◽  
Author(s):  
Hui Yan ◽  
Heinz W. Siesler
Keyword(s):  
Near Infrared ◽  
Raw Materials ◽  
Discrimination Performance ◽  
Principal Component ◽  
Identification Performance ◽  
Score Plot ◽  
Discrimination Capability ◽  
Standard Normal Variate ◽  
Correct Assignment ◽  
Standard Normal

For sustainable utilization of raw materials and environmental protection, the recycling of the most common polymers—polyethylene (PE), polypropylene (PP), polyethylene terephthalate (PET), polyvinyl chloride (PVC), and polystyrene (PS)—is an extremely important issue. In the present communication, the discrimination performance of the above polymer commodities based on their near-infrared (NIR) spectra measured with four real handheld (<200 g) spectrometers based on different monochromator principles were investigated. From a total of 43 polymer samples, the diffuse reflection spectra were measured with the handheld instruments. After the original spectra were pretreated by second derivative and standard normal variate (SNV), principal component analysis (PCA) was applied and unknown samples were tested by soft independent modeling of class analogies (SIMCA). The results show that the five polymer commodities cluster in the score plots of their first three principal components (PCs) and, furthermore, samples in calibration and test sets can be correctly identified by SICMA. Thus, it was concluded that on the basis of the NIR spectra measured with the handheld spectrometers the SIMCA analysis provides a suitable analytical tool for the correct assignment of the type of polymer. Because the mean distance between clusters in the score plot reflects the discrimination capability for each polymer pair the variation of this parameter for the spectra measured with the different handheld spectrometers was used to rank the identification performance of the five polymer commodities.

scholarly journals PENDUGAAN KANDUNGAN ENERGI BRUTO TEPUNG IKAN MENGGUNAKAN TEKNOLOGI Near Infrared (NIR) (Prediction of the Gross Energy for Fishmeal using Near Infrared Reflectant (NIR) Technology)

2016 ◽  
Vol 1 (1) ◽  
pp. 33
Author(s):  
Ati Atul Quddus
Keyword(s):  
Standard Error ◽  
Coefficient Of Variation ◽  
Near Infrared ◽  
Animal Feed ◽  
Principal Component Regression ◽  
Principal Component ◽  
Calibration Equation ◽  
Bomb Calorimeter ◽  
Gross Energy ◽  
Pcr Method

Abstrak Penelitian ini bertujuan untuk menduga kandungan energi bruto tepung ikan untuk bahan pakan ternak menggunakan teknologi Near Infrared (NIR). Tepung ikan yang digunakan dalam penelitian ini diperoleh dari poultry shop yang ada di beberapa daerah di Indonesia dan industri pakan ternak. Penelitian ini menggunakan 50 tepung ikan. Tiga puluh lima sampel digunakan untuk kalibrasi, sedangkan 15 sampel digunakan untuk validasi. Pengukuran NIR reflektan menggunakan sistem NIR. Energi bruto diukur menggunakan bomb calorimeter. Data dianalisis dengan menggunakan regresi linier berganda (RLB) dan Principal Component Regression (PCR). Persamaan kalibrasi dari reflektan dianalisis menggunakan 29 panjang gelombang untuk memprediksi energi bruto. Hasil dari validasi menunjukkan akurasi yang tinggi dengan standar eror dan koefisien variasi untuk energi bruto yaitu 6,6 Kkal/Kg dan 0,2%. Persamaan kalibrasi dari metode PCR menggunakan data absorban. Hasil dari validasinya menunjukkan kurang akurasi dengan nilai standar eror dan koefisien variasi yaitu 119,2 Kkal/kg dan 4,16%. Kata kunci : energi bruto, NIR, RLB, PCR Abstract This experiment was aimed to predict gross energy (GE) content of fishmeal by using Near Infrared (NIR) technology. Fishmeal that was used in this experiment was obtained from the poultry shop in several regions in Indonesia and from animal feed industries. This experiment was conducted by using 50 fishmeals. Thirty five samples out of 50 samples fishmeal was used to develop the NIR of calibration and the rest 15 samples was used to test the accuracy of the calibration. NIR reflectant was measured by NIR system. Gross energy was measured by bomb calorimeter. Collected data were analyzed by using multivariate linier regression (MLR) and principal component regression (PCR). Calibration equation of reflectant was analyzed by using 29 wavelengths for predicting GE. The results of the validation indicated high accuracy with standard error and coefficient of variation for GE: SEp = 6.6 Kkal/Kg, CV = 0.2 % . Calibration equation was obtained from PCR method by using absorbent data. The result of the validation indicated less accuracy with standard error and coefficient of variation for GE: SEp = 119.92 Kkal/Kg, CV = 4.16% . Keywords : Gross Energy, Near infrared Reflectant (NIR), fishmeal, Multivariate Linier Regression (MLR), Principal Component Regression (PCR)

scholarly journals FTIR spectroscopy for quality evaluation of sports supplements on the Polish market

2020 ◽  
pp. 177-185
Author(s):  
Krzysztof Wójcicki
Keyword(s):  
Ftir Spectroscopy ◽  
Principal Component ◽  
Ftir Spectra ◽  
Protein Supplements ◽  
Sports Supplements ◽  
Calibration And Validation ◽  
Standard Normal Variate ◽  
Standard Normal ◽  
Spectrum Region ◽  
Branched Chain

Introduction. Our study aimed to apply medium infrared (MIR/FTIR) spectroscopy to evaluate the quality of various sports supplements available in the Polish shops and gyms. Study objects and methods. The study objects included forty-eight sports supplements: whey (15 samples), branched-chain amino acids (12 samples), creatine (3 samples), mass gainers (6 samples), and pre-workouts (12 samples). First, we determined the protein quantity in individual whey supplements by the Kjeldahl method and then correlated the results with the measured FTIR spectra by chemometric methods. The principal component analysis (PCA) was used to distinguish the samples based on the measured spectra. The samples were grouped according to their chemical composition. Further, we correlated the spectra with the protein contents using the partial least squares (PLS) regression method and mathematic transformations of the FTIR spectral data. Results and discussion. The analysis of the regression models confirmed that we could use FTIR spectra to estimate the content of proteins in protein supplements. The best result was obtained in a spectrum region between 1160 and 2205 cm–1 and after the standard normal variate normalization. R2 for the calibration and validation models reached 0.85 and 0.76, respectively, meaning that the models had a good capability to predict protein content in whey supplements. The RMSE for the calibration and validation models was low (2.7% and 3.7%, respectively). Conclusion. Finally, we proved that the FTIR spectra applied together with the chemometric analysis could be used to quickly evaluate the studied products.

Multivariate Analysis of Attenuated Total Reflection—Fourier Transform Infrared Spectroscopic Data to Confirm the Origin of Honeys

2008 ◽  
Vol 62 (10) ◽  
pp. 1115-1123 ◽  
Author(s):  
Siobhán Hennessy ◽  
Gerard Downey ◽  
Colm O'Donnell
Keyword(s):  
Fourier Transform ◽  
Discriminant Analysis ◽  
Fourier Transform Infrared ◽  
Total Reflection ◽  
Attenuated Total Reflection ◽  
Correct Classification ◽  
Standard Normal Variate ◽  
Standard Normal ◽  
Ft Ir ◽  
Solids Content

Fourier transform infrared (FT-IR) spectroscopy and chemometrics were used to verify the origin of honey samples ( n = 150) from Europe and South America. Authentic honey samples were collected from five sources, namely unfiltered samples from Mexico in 2004, commercially filtered samples from Ireland and Argentina in 2004, commercially filtered samples from the Czech Republic in 2005 and 2006, and commercially filtered samples from Hungary in 2006. Samples were diluted with distilled water to a standard solids content (70° Brix) and their spectra (2500–12 500 nm) recorded at room temperature using an FT-IR spectrometer equipped with a germanium attenuated total reflection (ATR) accessory. First- and second-derivative and standard normal variate (SNV) data pretreatments were applied to the recorded spectra, which were analyzed using partial least squares (PLS) regression analysis, factorial discriminant analysis (FDA), and soft independent modeling of class analogy (SIMCA). In general, when an attenuated wavelength range (6800–11 500 nm) rather than the whole spectrum (2500–12 500 nm) was studied, higher correct classification rates were achieved. An overall correct classification of 93.3% was obtained for honeys by PLS discriminant analysis, while FDA techniques correctly classified 94.7% of honey samples. Correct classifications of up to 100% were achieved using SIMCA, but models describing some classes had very high false positive rates.

scholarly journals Automatic and Rapid Discrimination of Cotton Genotypes by Near Infrared Spectroscopy and Chemometrics

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Hai-Feng Cui ◽  
Zi-Hong Ye ◽  
Lu Xu ◽  
Xian-Shu Fu ◽  
Cui-Wen Fan ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Classification Accuracy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Standard Normal Variate ◽  
Pattern Recognition Methods ◽  
Standard Normal ◽  
Cotton Genotypes ◽  
Rapid Discrimination

This paper reports the application of near infrared (NIR) spectroscopy and pattern recognition methods to rapid and automatic discrimination of the genotypes (parent, transgenic, and parent-transgenic hybrid) of cotton plants. Diffuse reflectance NIR spectra of representative cotton seeds (n=120) and leaves (n=123) were measured in the range of 4000–12000 cm−1. A practical problem when developing classification models is the degradation and even breakdown of models caused by outliers. Considering the high-dimensional nature and uncertainty of potential spectral outliers, robust principal component analysis (rPCA) was applied to each separate sample group to detect and exclude outliers. The influence of different data preprocessing methods on model prediction performance was also investigated. The results demonstrate that rPCA can effectively detect outliers and maintain the efficiency of discriminant analysis. Moreover, the classification accuracy can be significantly improved by second-order derivative and standard normal variate (SNV). The best partial least squares discriminant analysis (PLSDA) models obtained total classification accuracy of 100% and 97.6% for seeds and leaves, respectively.

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