• Food Science of Animal Resources
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• v.42 no.4
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• pp.655-671
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• 2022

There are differences of spectral characteristics between different types of meat cut, which means the model established using only one type of meat cut for meat quality prediction is not suitable for other meat cut types. A novel portable visible and near-infrared (Vis/NIR) optical system was used to simultaneously predict multiple quality indicators for different commercial meat cut types (silverside, back strap, oyster, fillet, thick flank, and tenderloin) from Small-tailed Han sheep. The correlation coefficients of the calibration set (R_c) and prediction set (R_p) of the optimal prediction models were 0.82 and 0.81 for pH, 0.88 and 0.84 for L^*, 0.83 and 0.78 for a^*, 0.83 and 0.82 for b^*, 0.94 and 0.86 for cooking loss, 0.90 and 0.88 for shear force, 0.84 and 0.83 for protein, 0.93 and 0.83 for fat, 0.92 and 0.87 for moisture contents, respectively. This study demonstrates that Vis/NIR spectroscopy is a promising tool to achieve the predictions of multiple quality parameters for different commercial meat cut types.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1275-1275
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• 2001

The pH value of grass silages is one important parameter to determine the quality of the forages. In an attempt to use NIRS spectra taken for other quality parameter of grass silage it has been shown that a good correlation between NIR spectra of the dried forage and pH value of the fresh forage could be determined. Further investigations revealed that the B coefficients of the pH value calibration were almost the same as the B coefficients of the sugar calibration multiplied with -1. And indead the pH value - in the fresh sample material - of the calibration set is strongly correlated with the sugar concentration - in the dried sample material. It is concluded that next to scientific tools in research the scientist and the user of NTRS equippment has to scrutinze his own work. Examples are given. NIRS is a powerfull technique, but pitfalls are present in surplus.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.3112-3112
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• 2001

Near-Infrared Spectroscopy (NIRS) was investigated aiming at the identification of falsified drugs. The identification is based on comparison of the NIR spectrum of a sample with a typical spectra of an authentic drug using multivariate modelling and classification algorithms (PCA/SIMCA). Two spectrophotometers (Brimrose - Luminar 2000 and 2030), based on acoustic-optical filter (AOTF) technology, sharing the same controlling computer, software (Brimrose - Snap 2.03) and the data acquisition electronics, were employed. The Luminar 2000 scans the range 850 1800 nm and was employed for transmitance/absorbance measurements of liquids with a transflectance optical bundle probe with total optical path of 5 mm and a circular area of 0.5 $\textrm{cm}^2$. Model 2030 scans the rage 1100 2400 nm and was employed for reflectance measurement of solids drugs. 300 spectra, acquired in about 20 s, were averaged for each sample. Chemometric treatment of the spectral data, modelling and classification were performed by using the Unscrambler 7.5 software (CAMO Norway). This package provides the Principal Component Analysis (PCA) and SIMCA algorithms, used for modelling and classification, respectively. Initially, NIRS was evaluated for spectrum acquisition of various drugs, selected in order to accomplish the diversity of physico-chemical characteristics found among commercial products. Parameters which could affect the spectra of a given drug (especially if presented as solid tablets) were investigated and the results showed that the first derivative can minimize spectral changes associated with tablet geometry, physical differences in their faces and position in relation to the probe beam. The effect of ambient humidity and temperature were also investigated. The first factor needs to be controlled for model construction because the ambient humidity can cause spectral alterations that should cause the wrong classification of a real drug if the factor is not considered by the model.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1624-1624
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• 2001

Near-infrared spectroscopy (NIRS) was used to investigate the possibility for application in identification of apple cultivars. Three apple cultivars ‘Kamhong, Hwahong, and Fuji’ produced in Korea were scanned over the range of 1100-2500nm using NIRS (Infra Alzer 500). Two types of samples were used for scanning; one was apple with skin and the other was apple without skin. For cultivar identification, the NIR absorbance spectrums were analyzed by qualitative calibration in “Sesame” analysis program, and the various influence properties such as sugar contents, acidity, color, firmness, and micro-structure were compared in scanned samples. The ‘Kamhong’ cultivar could be identified from ‘Hwahong’ and ‘Fuji’ cultivars using the cluster model analysis. The test samples in calibration between ‘Kamhong’ and ‘Fuji’ cultivars could be completely identified. The test samples in calibration between ‘Kamhong’ and ‘Hwahong’ cultivars could be identified most of all. But, ‘Hwahong’ and ‘Fuji’ cultivars could not be quite classified each other. The apple skin influenced the identification process of apple cultivars. The samples without skin were more difficult to classify in calibration than the samples with skin. The physicochemical properties of apple cultivars showed like the result of identification in calibration using NIRS. Some physicochemical properties of ‘Kamhong’ cultivar were different from those of the other cultivars. Those of ‘Hwahong’ and ‘Fuji’ cultivars showed. similar to each other. The sucrose contents of ‘Kamhong’ cultivar were higher and the fructose contents and firmness of skin and flesh were lower than those of the others. The hypodermis layer of skin in ‘Kamhong’ cultivar was thinner than those of the others. In this studies, the identification of all apple cultivars by NIRS was not quite accurate because of the physicochemical properties which were different in the same cultivar, and inconsistent patterns by culivars in some properties. To solve these problems in NIRS application for apple cultivar identification, further study should be focused on the use of peculiar properties among the apple cultivars.

• Korean Journal of Soil Science and Fertilizer
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• v.34 no.4
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• pp.273-277
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• 2001

Traditional methods of chemical analysis for the soil properties take time and produce harmful waste. The purpose of this research was to evaluate an NIR technique for measuring some soil properties that are rapid and accurate in soil fertility assessments. The NIR instrument (InfraAlyzer 500, Bran & Luebbe Co.) was used for obtaining spectral data from 140 finely ground soil for calibrations and validation estimating pH, CEC, extractable Ca, Mg, K, $SiO_2$, humic acid and EC. Partial least square regression analysis was used to develop a calibration of NIR spectroscopy method. The results indicated that NIR spectroscopy could be used as a routine nondestructive method quantitatively determining soil chemical properties quickly. However the NIR technique may require sample preparation to obtain even diffuse reflection spectra from the soil and data manipulations to obtain optimal predictions.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1244-1244
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• 2001

In linear discriminant analysis there are two important properties concerning the effectiveness of discriminant function modeling. The first is the separability of the discriminant function for different classes. The separability reaches its optimum by maximizing the ratio of between-class to within-class variance. The second is the stability of the discriminant function against noises present in the measurement variables. One can optimize the stability by exploring the discriminant variates in a principal variation subspace, i. e., the directions that account for a majority of the total variation of the data. An unstable discriminant function will exhibit inflated variance in the prediction of future unclassified objects, exposed to a significantly increased risk of erroneous prediction. Therefore, an ideal discriminant function should not only separate different classes with a minimum misclassification rate for the training set, but also possess a good stability such that the prediction variance for unclassified objects can be as small as possible. In other words, an optimal classifier should find a balance between the separability and the stability. This is of special significance for multivariate spectroscopy-based classification where multicollinearity always leads to discriminant directions located in low-spread subspaces. A new regularized discriminant analysis technique, the principal discriminant variate (PDV) method, has been developed for handling effectively multicollinear data commonly encountered in multivariate spectroscopy-based classification. The motivation behind this method is to seek a sequence of discriminant directions that not only optimize the separability between different classes, but also account for a maximized variation present in the data. Three different formulations for the PDV methods are suggested, and an effective computing procedure is proposed for a PDV method. Near-infrared (NIR) spectra of blood plasma samples from mastitic and healthy cows have been used to evaluate the behavior of the PDV method in comparison with principal component analysis (PCA), discriminant partial least squares (DPLS), soft independent modeling of class analogies (SIMCA) and Fisher linear discriminant analysis (FLDA). Results obtained demonstrate that the PDV method exhibits improved stability in prediction without significant loss of separability. The NIR spectra of blood plasma samples from mastitic and healthy cows are clearly discriminated between by the PDV method. Moreover, the proposed method provides superior performance to PCA, DPLS, SIMCA and FLDA, indicating that PDV is a promising tool in discriminant analysis of spectra-characterized samples with only small compositional difference, thereby providing a useful means for spectroscopy-based clinic applications.

• Near Infrared Analysis
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• v.1 no.1
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• pp.1-7
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• 2000

A convenient algorithm for optimizing wavelength selection in multiple linear regression (MLR) has been developed. MOP (MLP Optimization Program) has been developed to test all possible MLR calibration models in a given spectral range and finally find an optimal MLR model with external validation capability. MOP generates all calibration models from all possible combinations of wavelength, and simultaneously calculates SEC (Standard Error of Calibration) and SEV (Standard Error of Validation) by predicting samples in a validation data set. Finally, with determined SEC and SEV, it calculates another parameter called SAD (Sum of SEC, SEV, and Absolute Difference between SEC and SEV: sum(SEC+SEV+Abs(SEC-SEV)). SAD is an useful parameter to find an optimal calibration model without over-fitting by simultaneously evaluating SEC, SEV, and difference of error between calibration and validation. The calibration model corresponding to the smallest SAD value is chosen as an optimum because the errors in both calibration and validation are minimal as well as similar in scale. To evaluate the capability of MOP, the determination of benzene content in unleaded gasoline has been examined. MOP successfully found the optimal calibration model and showed the better calibration and independent prediction performance compared to conventional MLR calibration.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1616-1616
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• 2001

To select kernels for breeding that have required constituent content from either naturally distributed samples or artificially mutated ones, it is necessary to process batch samples in a short time. The constituent content of single-kernel grains such as wheat and rice has been determined using conventional bench type NIR instruments; however, it takes a lot of time and effort. Shizuoka Seiki (Fukuroi-city, Japan) and NFRI (National Food Research Institute) of MAFF (Ministry of Agriculture, forestry and Fisheries of Japan) have jointly developed a continuous high-speed single-kernel brown rice sorting machine based on rice protein content. It consists of several sections such as a feeding mechanism, measuring unit, sorting mechanism and controlling PC. The feeding mechanism picks up single-kernel brown rice from the hopper (maximum of 5kg storage capacity) and sends it to the measuring unit. A spectrum of the brown rice is obtained in the measuring unit, which consists of a near-infrared array sensor. The brown rice is then sorted in the sorting mechanism based on its protein content estimated by the controlling PC. In the present study, measuring speed was approximately 500ms for the full spectrum range and overall sorting speed was approximately 2.8s for one kernel. Accuracy of estimation was approximately SEP=0.5% of dry matter protein content for nonglutinous rice.

• Asian-Australasian Journal of Animal Sciences
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• v.14 no.6
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• pp.858-861
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• 2001

Near Infrared spectroscopy was applied to the samples of processed pork to see the effect of grinding on chemical components analyses. Data from conventional chemical analyses of moisture, fat, protein, NaCl were put into calibration model by NIR of reflectance mode. The other properties observed were pH and color parameters ($L^*,\;a^*,\;b^*$). Spectral ranges of 400~2500 nm and 400~1100 nm were compared for color parameters. Spectral ranges of 400~2500 nm and 1100~2500 nm were compared for chemical components and pH. Different spectral ranges caused little changes in the coefficients of determination or standard errors. $R^{2,}s$ of calibration models for color parameters were in the range of 0.97 to 1.00. $R^{2,}s$ of calibration models of intact sausages for moisture, protein, fat, NaCl and pH were 0.98, 0.89, 0.95, 0.73 and 0.77, respectively using spectra at 1100~2500 nm. $R^{2,}s$ of calibration models of ground sausages for moisture, protein, fat, NaCl and pH were 0.97, 0.91, 0.97, 0.42 and 0.56, respectively using spectra at 1100~2500 nm.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.2
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• pp.152.1-152.1
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• 2011

We report preliminary results of long-slit near-infrared (NIR) spectroscopy of Luminous Blue Variables (LBVs) with moderate resolution of R ~ 2400. We obtained Jshort (1.04-1.26 micron) and Ks (2.02-2.31 micron) band spectra of 4 LBVs and 3 LBV candidates in Southern hemisphere using IRIS2, infrared imager and spectrograph, mounted on the 4-m Anglo-Australian Telescope. All targets are fairly bright in NIR so that we can obtain high signal-to-noise ratio for clear line detection and modeling. They are also widely distributed in the HR diagram so that we can compare the spectral properties of LBVs in different temperature and luminosity ranges. Among them, we present the results of two well-known LBVs AG Car and HR Car. Their spectra show similar properties with hydrogen, He I, and metallic lines such as Fe II and Mg II, most of them in emission. We discuss, in particular, the He I 1.083 micron lines formed in stellar wind because these two LBVs show large variation in their He I line intensities, compared to previous studies. Since the He I 1.083 line is known to be anticorrelated with the photometric variation of LBVs, strong line intensities with P-Cygni profiles in both stars indicate that they are now near the visual minimum phase. We model the obtained spectra using non-LTE atmosphere code CMFGEN of Hillier (1998) to derive stellar parameters such as wind velocity and mass loss rate, and discuss the long-term variability of stellar parameters of these LBVs. deduced from our otometric solution.