• Journal of Biosystems Engineering
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• v.40 no.2
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• pp.153-158
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• 2015

Purpose: The research presented in this paper applied the chemometric analysis to the near-infrared spectral data from line-scanned hyperspectral images of beef and fish meals in animal feeds. The chemometric statistical models were developed to distinguish beef meals from fish ones. Methods: The meal samples of 40 fish meals and 15 beef meals were line-scanned to obtain hyperspectral images. The spectral data were retrieved from each of 3600 pixels in the Region of Interest (ROI) of every sample image. The wavebands spanning 969 nm to 1551 nm (across 176 spectral bands) were selected for chemometric analysis. The partial least squares regression (PLSR) and the principal component analysis (PCA) methods of the chemometric analysis were applied to the model development. The purpose of the models was to correctly classify as many beef pixels as possible while misclassified fish pixels in an acceptable amount. Results: The results showed that the success classification rates were 97.9% for beef samples and 99.4% for fish samples by the PLSR model, and 85.1% for beef samples and 88.2% for fish samples by the PCA model. Conclusion: The chemometric analysis-based PLSR and PCA models for the hyperspectral image analysis could differentiate beef meals from fish ones in animal feeds.

• Food Science of Animal Resources
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• v.42 no.4
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• pp.672-688
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• 2022

The objective of this study was to explore the potential of front face fluorescence spectroscopy (FFFS) as rapid, non-destructive and inclusive technique along with multi-variate analysis for predicting meat adulteration. For this purpose (FFFS) was used to discriminate pure minced beef meat and adulterated minced beef meat containing (1%, 2%, 3%, 4%, 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100%) of chicken meat as an adulterant in uncooked beef meat samples. Fixed excitation (290 nm, 322 nm, and 340 nm) and fixed emission (410 nm) wavelengths were used for performing analysis. Fluorescence spectra were acquired from pure and adulterated meat samples to differentiate pure and binary mixtures of meat samples. Principle component analysis, partial least square regression and hierarchical cluster analysis were used as chemometric tools to find out the information from spectral data. These chemometric tools predict adulteration in minced beef meat up to 10% chicken meat but are not good in distinguishing adulteration level from 1% to 5%. The results of this research provide baseline for future work for generating spectral libraries using larger datasets for on-line detection of meat authenticity by using fluorescence spectroscopy.

• Bulletin of the Korean Chemical Society
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• v.18 no.7
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• pp.695-702
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• 1997

The objective of this work is to determine the sugar profiles in commercial fruit juices, and to obtain chemometric characteristics. Sugar compositions of fruit juices were determined by HPLC-RID and GC-FID via methoxymation and trimethylsilylation with BSTFA. The appearance of multiple peaks in GC analysis for carbohydrates was disadvantageous as described in earlier literatures. Fructose, glucose, and sucrose were major carbohydrates in most fruit juices. Glucose/fructose ratios obtained by GC were lower than those by HPLC. Orange juices are similar to pineapple juices in the sugar profiles. However, grape juices are characterized by its lower or no detectable sucrose content. In addition, it was also found that unsweeten juices contained considerable level of sucrose. Chemometric technique such as principal components analysis was applied to provide an overview of the distinguishability of fruit juices based on HPLC or GC data. Principal components plot showed that different fruit juices grouped into distinct cluster. Principal components analysis was very useful in fruit juices industry for many aspects such as pattern recognition, detection of adulterants, and quality evaluation.

• Analytical Science and Technology
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• v.33 no.2
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• pp.98-107
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• 2020

Methamphetamine (MA) is currently the most abused illicit drug in Korea. MA is produced by chemical synthesis, and the final target drug that is produced contains small amounts of the precursor chemicals, intermediates, and by-products. To identify and quantify these trace compounds in MA seizures, a practical and feasible approach for conducting chromatographic fingerprinting with a suite of traditional chemometric methods and recently introduced machine learning approaches was examined. This was achieved using gas chromatography (GC) coupled with a flame ionization detector (FID) and mass spectrometry (MS). Following appropriate examination of all the peaks in 71 samples, 166 impurities were selected as the characteristic components. Unsupervised (principal component analysis (PCA), hierarchical cluster analysis (HCA), and K-means clustering) and supervised (partial least squares-discriminant analysis (PLS-DA), orthogonal partial least squares-discriminant analysis (OPLS-DA), support vector machines (SVM), and deep neural network (DNN) with Keras) chemometric techniques were employed for classifying the 71 MA seizures. The results of the PCA, HCA, K-means clustering, PLS-DA, OPLS-DA, SVM, and DNN methods for quality evaluation were in good agreement. However, the tested MA seizures possessed distinct features, such as chirality, cutting agents, and boiling points. The study indicated that the established qualitative and semi-quantitative methods will be practical and useful analytical tools for characterizing trace compounds in illicit MA seizures. Moreover, they will provide a statistical basis for identifying the synthesis route, sources of supply, trafficking routes, and connections between seizures, which will support drug law enforcement agencies in their effort to eliminate organized MA crime.

• KSBB Journal
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• v.21 no.1 s.96
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• pp.59-67
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• 2006

2D spectrofluorometer produces many spectral data during fermentation processes. The fluorescence spectra are analyzed using chemometric methods such as principal component analysis (PCA), principal component regression (PCR) and partial least square regression (PLS). Analysis of the spectral data by PCA results in scores and loadings that are visualized in score-loading plots and used to monitor a few fermentation processes by S. cerevisae and recombinant E. coli. Two chemometric models were established to analyze the correlation between fluorescence spectra and process variables using PCR and PLS, and PLS was found to show slightly better calibration and prediction performance than PCR.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1133-1133
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• 2001

Fruit sweetness, as indexed by total soluble solids (TSS), and fruit acidity are key factors in the description of the fruit eating quality. Our group has been using short wave NIR spectroscopy (SW-NIR; 700-1100 nm) in combination with chemometric methods (PLS and MLR) for the non-invasive determination of the fruit eating quality (1,2). In order to further improve calibration performance, we have investigated SW-NIR spectra of sucrose and D-glucose. In previous reports on the band assignment for these sugars in the 1100-2500 nm spectral region (3-7), it has been established that change in concentration, temperature and physical state of sugars reflects on the shape and position of the spectral bands in the whole NIR region(5-7). The effect of change in concentration and temperature of individual sugar solutions and sugar spiked Juice samples was analysed using combined spectroscopic (derivative, difference, 2D spectroscopy) and linear regression chemometric (PLS, MLR) techniques. The results have been compared with the spectral data of a range of fruit types, varying in TSS content and temperature. In the 800-950 nm spectral region, the B-coefficients for apples, peaches and nectarines resemble those generated in a calibration of pure sucrose in water (Fig. 1). As expected, these fruits exhibit better calibration and prediction results than those in which the B-coefficients were poorly related to those for sugar.(Figure omitted).

• Analytical Science and Technology
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• v.10 no.5
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• pp.362-369
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• 1997

Chemometric technique was applied to the sugar composition in five honeys of known botanical or geographical origin following HPLC. Fructose and glucose were predominant carbohydrates in honeys, and small amount of sucrose was also detected in one sample. Sugar contents in honeys samples were compared by the geographical or botanical origin. Fructose/glucose ratio ranged from 0.99 to 1.55 was obtained and these results are in good agreement with the ratio of literature. The plot of principal components analysis(PCA) showed that different honey samples grouped into distinct cluster by the geographical or botanical origin. Increasing the first or second principal component score, higher amount of sugar or less fructose/glucose ratio was observed in PCA plot. Chemometric approach was very useful to provide pattern recognition of sugar profile or quality indices of honey sample and to detect adulteration.

• Proceedings of the Korean Society of Crop Science Conference
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• 2023.04a
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• pp.179-179
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• 2023

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1284-1284
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• 2001

The transesterification of molten ethylene/vinylacetate (EVA) copolymers by octanol as a reagent and sodium methoxide as a catalyst in an extruder has been monitored by on-line near infrared (NIR) spectroscopy. A total of 60 NIR spectra were acquired for 37 minutes with the last spectrum recorded 31 minutes after the addition of octanol and catalyst was stopped. The experimental spectra show strong baseline fluctuations which are corrected for by multiplicative scatter correction (MSC). The chemometric methods of orthogonal projection approach (OPA) and multivariate curve resolution (MCR) were used to resolve the spectra and to derive concentration profiles of the species. The detailed analysis reveals the absence of completely pure variables that leads to small errors in the calculation of pure spectra. The initial estimation of a concentration that is necessary as an input parameter for MCR also presents a non-trivial task. We obtained results that were not ideal but applicable for practical concentration control. They enable a fast monitoring of the process in real-time and resolve the spectra of the EVA copolymer and the ethylene/vinyl alcohol (EVAL) copolymer to be very close to the reference spectra. The chemometric methods used and the decomposed spectra are discussed in detail.

• Bulletin of the Korean Chemical Society
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• v.29 no.1
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• pp.61-68
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• 2008

High initial (2 minutes after iv injection) brain-uptake of PET agents is required to deliver the agent to binding sites in brain tissue but, for quantification of the specific binding, relatively rapid washout of free and non-specifically bound PET agents from the brain (30 minutes after injection) also is required. In order to compare the physicochemical properties of the PET agents which are responsible for early brain-uptake and rapid washout, respectively, chemometric analysis on brain-uptake of PET agents was performed via a classical VolSurf approach. According to the PCA and PLS results, high 2-30 min brain-uptake ratio seems to be related to the large hydrophobic regions in the PET agents which are not confined to a particular surface.