• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1511-1511
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• 2001

Energy content, expressed as calories per gram, is an important part of the evaluation and marketing of foods in developed countries. Currently accepted methods of measurement of energy by U.S. food labeling legislation include measurement of gross calories by bomb calorimetry with an adjustment for undigested protein and by calculation using specific factors for the energy values of protein, carbohydrate less the amount of insoluble dietary fiber, and total fat. The ability of NIRS to predict the energy value of diverse, processed and unprocessed cereal food products was investigated. NIR spectra of cereal products were obtained with an NIR Systems monochromator and the wavelength range used for analysis was 1104-2494 nm. Gross energy of the foods was measured by oxygen bomb calorimetry (Parr Manual No. 120) and expressed as calories per gram (CPGI, range 4.05-5.49 cal/g). Energy value was adjusted for undigested protein (CPG2, range 3.99-5.38 cal/g) and undigested protein and insoluble dietary fiber (CPG3, range 2.42-5.35 cal/g). Using a multivariate analysis software package (ISI International, Inc.) partial least squares models were developed for the prediction of energy content. The standard error of cross validation and multiple coefficient of determination for CPGI using modified partial least squares regression (n=127) was 0.060 cal/g and 0.95, respectively, and the standard error of performance, coefficient of determination, bias and slope using an independent validation set (n=59) were 0.057 cal/g, 0.98, -0.027 cal/g and 1.05 respectively. The PLS loading for factor 1 (Pearson correlation coefficient 0.92) had significant absorption peaks correlated to C-H stretch groups in lipid at 1722/1764 nm and 2304/2346 nm and O-H groups in carbohydrate at 1434 and 2076 nm. Thus the model appeared to be predominantly influenced by lipid and carbohydrate. Models for CPG2 and CPG3 showed similar trends with standard errors of performance, using the independent validation set, of 0.058 and 0.088 cal/g, respectively, and coefficients of determination of 0.96. Thus NIRS provides a rapid and efficient method of predicting energy content of diverse cereal foods.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1156-1156
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• 2001

This work aimed to prove the feasibility of NIR spectroscopy to detect vegetable protein isolates (soy, pea and wheat) in milk powder. Two hundred and thirty-nine samples of genuine and adulterated milk powder (NIZO, Ede, NL) were analysed by NIRS using an InfraAlyzer 500 (Bran+Luebbe). NIR spectra were collected at room temperature, and data were processed by using Sesame Software (Bran+Luebbe). Separated calibrations for each non-milk protein added, in the range of 0-5%, were calculated. NIR data were processed by using Sesame Software (Bran+Luebbe). Prediction and validation were made by using a set of samples not included into the calibration set. The best calibrations were obtained by the PLSR. The type of data pre-treatment (normalisation, 1$\^$st/ derivative, etc..) was chosen to optimize the calibration parameters. NIRS technique was able to predict with good accuracy the percentage of each vegetable protein added to milk powder (soy: R$^2$ 0.994, SEE 0.193, SEcv 0.301, RMSEPall 0.148; pea: R$^2$ 0.997, SEE 0.1498, SEcv 0.207, RMSEPall 0.148, wheat: R$^2$ 0.997, SEE 0.1418, SEcv 0.335, RMSEPall 0.149). Prediction results were compared to those obtained using other two techniques: capillary electrophoresis and competitive ELISA. On the basis of the known true values of non-vegetable protein contents, the NIRS was able to determine more accurately than the other two techniques the percentage of adulteration in the analysed samples.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1286-1286
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• 2001

Despite extensive theoretical and experimental studies the structure of the protein-solvent interface is subject of many controversy. Understanding the processes that occur in aqueous solution requires understanding of the solvent influence on the structure of protein. The aim of this study is to investigate the applicability of NIR methods in the study of hydration phenomena in protein solutions. Temperature-induced changes in NIR spectra of -lactoglobulin (BLG) in aqueous solutions have been investigated by means of two-dimensional correlation spectroscopy (2DCOS) and principal component analysis (PCA). With the temperature increase the balance of forces between the BLG's interaction with itself and the BLGs interaction with its environment is disrupted leading to BLG unfolding. Significant differences of 2D signals and distinct discrepancies of loading on PC1 and PC2 were observed as a result of temperature increase. In the native folded conformation of BLC, most of the nonpolar amino acids are hidden in the centre of the structure, out of contact with water molecules, while charged groups are outside, in the contact with water. The polar groups promote low density Ih-type structure in the water outside this first hydration shell. When BLG unfolds it assumes a more extended configuration on which the previously buried nonpolar groups are exposed to water and promote the higher density II-type structure outside its first shell. Detailed assignments of bands attributed to the bulk water, different states of the hydrated water and the changed conformation of BLG are proposed.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1519-1519
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• 2001

The aim of this work was to test the feasibility of NIRS in analysing textural characteristics of “Pasta Filata” cheese during the shelf-life. For this purpose, 128 samples of “Pasta Filata” cheese, subdivided into two sets on the basis of the wax used to avoid mechanical damages (paraffin, biodegradable wax), were analysed by using an InfraAlyzer 500 (Bran+Luebbe). Analyses were performed at room temperature. Samples were cut into small cylinders (D=3.2 cm, height = 1 cm), in agreement with literature information. Data were processed by using Sesame Software (Bran+Luebbe). Samples were analysed, during the shelf-life, at 90 and 120 days. In parallel, textural characteristics were detected carrying out a compression method by using an Universal Testing Machine Instron model 4301 (Instron Corporation, Canton, Massachusetts). As compression probe was used a cylinder (D = 5.8 cm, height = 3.7 cm) and a speed rate of 20mm/min was applied. The load at 20 mm of compression was recorded on sample cylinders of 1.7 cm (D) by 2 cm (height). Qualitative analysis of full spectra showed the possibility to gather samples on the basis of the days of shelf-life. The textural characteristics of cheese during the shelf-life was evaluated by comparing NIRS data with rheological results. The best correlation was obtained applying MLR to the first derivative of normalized absorbance values at seven wavelengths. Load values were plotted against the NIR prediction values based on first derivatives. NIRS proved to be an useful tool in classifying samples on the basis of the shelf-life period as well as in predicting their textural characteristics ($R^2$= 0.916, SEC = 0.192, SEP = 0.248, SEV = 0.345).

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1255-1255
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• 2001

The study used 356 veal calf meat samples received from Finland (n=16), France (n=109), Italy (n=81) and The Netherlands (n=150). Calves were raised under experimental protocols that compared feeding and housing practices normally used in each county to treatments aiming at improving animal welfare. Samples were taken at the $8^{th}$ rib of Longissimus thoracis muscle 24h after slaughter, They were kept refrigerated ( $2-4^{\circ}C$) under vacuum package for 6d and then frozen ($-20^{\circ}C$) until meat quality evaluation. Measurements included pH, color (Hunter Lab system), shear force, chemical composition (DM, Ash, Ether Extract, collagen and haematin content), weight and area cooking losses and a sensory evaluation by a group of panelists. A sample of meat was ground with a blade mill and scanned in duplicate between 1100 and 1498 nm (FOSS NIR Systems 5000). WinISI software was used to develop a discriminating equation using NIR spectra (SNV-detrend, derivative=1, gap=4nm, smooth=4nm). The Proc ANOVA and DISCRIM of SAS were used for all the laboratory determinations. County of production had a significant (P<0.01) effect on all the parameters. However, discriminant analysis using any or few laboratory parameters resulted in great errors of county classification. A more accurate (98.8%) classification was obtained only when using all the laboratory parameters. NIRS classified correctly 354 of the 356 samples (99.4%). Provided with a larger data set, NIRS could be used to identify country of production of veal meat.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.37 no.5
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• pp.359-365
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• 2004

To study the lipid adsorption characteristic of chitosans with different molecular weights and the degrees of deacetylation, in vitro test and near-infrared (NIR) spectroscopic analysis have been performed for the measurement of lipid adsorption characteristics of chitosan. The degrees of deacetylation in chitosans were $70{\%},\;85{\%}\;and\;92{\%}$ at different deacetylation times (1 hr, 2 hrs, 3 hrs), respectively. The molecular weight of each chitosan was controlled by enzymatic hydrolysis, and then the molecular weight of the chitosan was 4 kDa. The bulk density, water holding capacity and fat binding capacity of each chitosan powder were $96.2-504.0{\%},\;374.4-1217.9{\%},\;and\;307.0-659.3{\%}$, respectively. The higher molecular weight of chitosan was exhibited the lower bulk density and the higher water and fat binding capacities. Bindinf capacities of chitosan powders to bile salts, cholesterol and linoleic acid were $41.2-63.3{\%},\;40.8-67.4{\%},\;42.6-72.6{\%}$, respectively. In NIR spectrum of lipid adsorbed chitosan the occurrence static eletronical binding between chitosan and lipid was identified by NIR spectrum peak induced from combination of carboxylic group in lipid and amino group in chitosan. In conclusion, the higher degree of deacetylation and molecular weight of chitosan showed the higher lipid binding capacity and the lipid adsorption of chitosan were occurred by combination of carboxylic group in lipids and amino group in chitosan.

• Journal of Biosystems Engineering
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• v.39 no.3
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• pp.184-193
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• 2014

Purpose: The aim of this study was to develop a technique for the non-destructive and rapid prediction of the moisture content in red pepper powder using near-infrared (NIR) spectroscopy and a partial least squares regression (PLSR) model. Methods: Three red pepper powder products were separated into three groups based on their particle sizes using a standard sieve. Each product was prepared, and the expected moisture content range was divided into six or seven levels from 3 to 21% wb with 3% wb intervals. The NIR reflectance spectra acquired in the wavelength range from 1,100 to 2,300 nm were used for the development of prediction models of the moisture content in red pepper powder. Results: The values of $R{_V}{^2}$, SEP, and RPD for the best PLSR model to predict the moisture content in red pepper powders of varying particle sizes below 1.4 mm were 0.990, ${\pm}0.487%$ wb, and 10.00, respectively. Conclusions: These results demonstrated that NIR spectroscopy and a PLSR model could be useful techniques for measuring rapidly and non-destructively the moisture content in red pepper powder.

• Journal of Biosystems Engineering
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• v.38 no.4
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• pp.312-317
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• 2013

Purpose: Conventional methods used to evaluate seeds viability are destructive, time consuming, and require the use of chemicals, which are not feasible to implement to process plant in seed industry. In this study, the effectiveness of Fourier transform near infrared (FT-NIR) spectroscopy to differentiate between viable and nonviable watermelon seeds was investigated. Methods: FT-NIR reflectance spectra of both viable and non-viable (aging) seeds were collected in the range of 4,000 - 10,000 $cm^{-1}$ (1,000 - 2,500 nm). To differentiate between viable and non-viable seeds, a multivariate classification model was developed with partial least square discrimination analysis (PLS-DA). Results: The calibration and validation set derived from the PLS-DA model classified viable and non-viable seeds with 100% accuracy. The beta coefficient of PLS-DA, which represented spectral difference between viable and non-viable seeds, showed that change in the chemical component of the seed membrane (such as lipids and proteins) might be responsible for the germination ability of the seeds. Conclusions: The results demonstrate the possibility of using FT-NIR spectroscopy to separate seeds based on viability, which could be used in the development of an online sorting technique.

• Journal of Animal Environmental Science
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• v.20 no.3
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• pp.117-124
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• 2014

The objectives were to assess the use of near-infrared reflectance spectroscopy (NIRS) as a tool for estimating nutrient compositions of corn kernel, and to apply an NIRS-based indium gallium arsenide array detector to the system for collecting spectra and analyzing calibration equations using equipments designed for field application. Partial Least Squares Regression (PLSR) was employed to develop calibration equations based on representative data sets. The kennard-stone algorithm was applied to induce a calibration set and a validation set. As a result, the method for structuring a calibration set significantly affected prediction accuracy. The prediction of chemical composition of corn kernel resulted in the following (kennard-stone algorithm: relative) moisture ($R^2=0.82$, RMSEP=0.183), crude protein ($R^2=0.80$, RMSEP=0.142), crude fat ($R^2=0.84$, RMSEP=0.098), crude fiber ($R^2=0.74$, RMSEP=0.098), and crude ash ($R^2=0.81$, RMSEP=0.048). Result of this experiment showed the potential of NIRS to predict the chemical composition of corn kernel.

• Journal of Biosystems Engineering
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• v.38 no.1
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• pp.48-54
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• 2013

Purpose: This study evaluated the feasibility of using a discrete wavelet transform (DWT) method as a preprocessing tool for visible/near-infrared spectroscopy (VIS/NIRS) with a spectroscopic transmittance dataset for predicting the internal quality of cherry tomatoes. Methods: VIS/NIRS was used to acquire transmittance spectrum data, to which a DWT was applied to generate new variables in the wavelet domain, which replaced the original spectral signal for subsequent partial least squares (PLS) regression analysis and prediction modeling. The DWT concept and its importance are described with emphasis on the properties that make the DWT a suitable transform for analyzing spectroscopic data. Results: The $R^2$ values and root mean squared errors (RMSEs) of calibration and prediction models for the firmness, sugar content, and titratable acidity of cherry tomatoes obtained by applying the DWT to a PLS regression with a set of spectra showed more enhanced results than those of each model obtained from raw data and mean normalization preprocessing through PLS regression. Conclusions: The developed DWT-incorporated PLS models using the db5 wavelet base and selected approximation coefficients indicate their feasibility as good preprocessing tools by improving the prediction of firmness and titratable acidity for cherry tomatoes with respect to $R^2$ values and RMSEs.