• Food Science and Preservation
• /
• v.30 no.2
• /
• pp.224-234
• /
• 2023

We analyzed the major quality characteristics of red pepper powders from various regions and predicted these characteristics nondestructively using shortwave infrared hyperspectral imaging (HSI) technology. We conducted partial least squares regression analysis on 70% (n=71) of the acquired hyperspectral data of the red pepper powders to examine the major quality characteristics. R_c² values of ≥0.8 were obtained for the ASTA color value (0.9263) and capsaicinoid content (0.8310). The developed quality prediction model was validated using the remaining 30% (n=35) of the hyperspectral data; the highest accuracy was achieved for the ASTA color value (R_p²=0.8488), and similar validity levels were achieved for the capsaicinoid and moisture contents. To increase the accuracy of the quality prediction model, we conducted spectrum preprocessing using SNV, MSC, SG-1, and SG-2, and the model's accuracy was verified. The results indicated that the accuracy of the model was most significantly improved by the MSC method, and the prediction accuracy for the ASTA color value was the highest for all the spectrum preprocessing methods. Our findings suggest that the quality characteristics of red pepper powders, even powders that do not conform to specific variables such as particle size and moisture content, can be predicted via HSI.

• The Korean Journal of Food And Nutrition
• /
• v.30 no.4
• /
• pp.719-727
• /
• 2017

The objective of this study was to investigate the effect of alcohol content in Makgeolli made with Chinese matrimony vine (M-CMV) on the sensory profile and consumer acceptability. The M-CMVs were prepared with 6, 7, 8, and 9% alcohol content. Descriptive analysis of M-CMV was performed with six trained panelists. Thirteen attributes were generated and their intensities were alcohol content dependent. The consumer acceptance test was conducted with 57 consumers. M-CMV samples with 7% alcohol had the highest acceptance rate (5.8) followed by 6% M-CMV (5.6). Commercial rice Makgeolli (CRM) had the lowest consumer acceptance. Consumers were divided into two groups by clustering analysis. The majority of consumers (n=38) preferred M-CMV and did not like the commercial sample. Only 19 consumers indicated high acceptance ratings for CRM. However, these consumers also preferred 6 and 7% M-CMV. Partial least-squares regression analysis revealed moderate attribute intensities were related to greater consumer acceptability. The optimal alcohol content for the greatest consumer acceptance predicted by linear regression was 6.7%.

• Horticultural Science & Technology
• /
• v.32 no.1
• /
• pp.105-114
• /
• 2014

We established a high throughput screening system of African yam tuber lines which contain high contents of total carotenoids, flavonoids, and phenolic compounds using ultraviolet-visible (UV-VIS) spectroscopy and Fourier transform infrared (FT-IR) spectroscopy in combination with multivariate analysis. The total carotenoids contents from 62 African yam tubers varied from 0.01 to $0.91{\mu}g{\cdot}g^{-1}$ dry weight (wt). The total flavonoids and phenolic compounds also varied from 12.9 to $229{\mu}g{\cdot}g^{-1}$ and from 0.29 to $5.2mg{\cdot}g^{-1}$dry wt. FT-IR spectra confirmed typical spectral differences between the frequency regions of 1,700-1,500, 1,500-1,300 and $1,100-950cm^{-1}$, respectively. These spectral regions were reflecting the quantitative and qualitative variations of amide I, II from amino acids and proteins ($1,700-1,500cm^{-1}$), phosphodiester groups from nucleic acid and phospholipid ($1,500-1,300cm^{-1}$) and carbohydrate compounds ($1,100-950cm^{-1}$). Principal component analysis (PCA) and subsequent partial least square-discriminant analysis (PLS-DA) were able to discriminate the 62 African yam tuber lines into three separate clusters corresponding to their taxonomic relationship. The quantitative prediction modeling of total carotenoids, flavonoids, and phenolic compounds from African yam tuber lines were established using partial least square regression algorithm from FT-IR spectra. The regression coefficients ($R^2$) between predicted values and estimated values of total carotenoids, flavonoids and phenolic compounds were 0.83, 0.86, and 0.72, respectively. These results showed that quantitative predictions of total carotenoids, flavonoids, and phenolic compounds were possible from FT-IR spectra of African yam tuber lines with higher accuracy. Therefore we suggested that quantitative prediction system established in this study could be applied as a rapid selection tool for high yielding African yam lines.

• Korean Journal of Agricultural Science
• /
• v.50 no.4
• /
• pp.675-696
• /
• 2023

Terpenoids, also referred to as terpenes, are a large family of naturally occurring chemical compounds present in the essential oils extracted from medicinal plants. In this study, a nondestructive methodology was created by combining ATR-FT-IR (attenuated total reflectance-Fourier transform infrared), and Raman spectroscopy for the terpenoids assessment in medicinal plants essential oils from ten different geographical locations. Partial least squares regression (PLSR) and support vector regression (SVR) were used as machine learning methodologies. However, a deep learning based model called as one-dimensional convolutional neural network (1D CNN) were also developed for models comparison. With a correlation coefficient (R²) of 0.999 and a lowest RMSEP (root mean squared error of prediction) of 0.006% for the prediction datasets, the SVR model created for FT-IR spectral data outperformed both the PLSR and 1 D CNN models. On the other hand, for the classification of essential oils derived from plants collected from various geographical regions, the created SVM (support vector machine) classification model for Raman spectroscopic data obtained an overall classification accuracy of 0.997% which was superior than the FT-IR (0.986%) data. Based on the results we propose that FT-IR spectroscopy, when coupled with the SVR model, has a significant potential for the non-destructive identification of terpenoids in essential oils compared with destructive chemical analysis methods.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.1171-1171
• /
• 2001

Quantitative analysis is an important requirement in exploration, mining and processing of minerals. There is an increasing need for the use of quantitative mineralogical data to assist with bore hole logging, deposit delineation, grade control, feed to processing plants and monitoring of solid process residues. Quantitative analysis using X-Ray Powder Diffraction (XRD) requires fine grinding and the addition of a reference material, or the application of Rietveld analysis to XRD patterns to provide accurate analysis of the suite of minerals present. Whilst accurate quantitative data can be obtained in this manner, the method is time consuming and limited to the laboratory. Mid infrared when combined with multivariant analysis has also been used for quantitative analysis. However, factors such as the absorption coefficients and refractive index of the minerals requires special sample preparation and dilution in a dispersive medium, such as KBr to minimize distortion of spectral features. In contrast, the lower intensity of the overtones and combinations of the fundamental vibrations in the near infrared allow direct measurement of virtually any solid without special sample preparation or dilution. Thus Near Infrared Spectroscopy (NIR) has found application for quantitative on-line/in line analysis and control in a range of processing applications which include, moisture control in clay and textile processing, fermentation processes, wheat analysis, gasoline analysis and chemicals and polymers. It is developing rapidly in the mineral exploration industry and has been underpinned by the development of portable NIR spectrometers and spectral libraries of a wide range of minerals. For example, iron ores have been identified and characterized in terms of the individual mineral components using field spectrometers. Data acquisition time of NIR field instruments is of the order of seconds and sample preparation is minimal. Consequently these types of spectrometers have great potential for in-line or on-line application in the minerals industry. To demonstrate the applicability of NIR field spectroscopy for quantitative analysis of minerals, a specific example on the quantification of lateritic bauxites will be presented. It has been shown that the application of Partial Least Squares regression analysis (PLS) to the NIR spectra can be used to quantify chemistry and mineralogy in a range of lateritic bauxites. Important, issues such as sampling, precision, repeatability, and replication which influence the results will be discussed.

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

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.1528-1528
• /
• 2001

Ginseng cultivated in different country or growing condition has generally different components such as saponin and protein, and it relates to efficacy and action. Protein content assumes by nitrogen content in ginseng radix. Nitrogen content could be determined by chemical analysis such as kjeldahl or extraction methods. However, these methods require long analysis time and result environmental pollution and sample damage. In this work we investigated possibility of non-destructive determination of nitrogen content in ginseng radix using near-infrared spectroscopy. Ginseng radix, root of Panax ginseng C. A. Meyer, was studied. Total 120 samples were used in this study and it was consisted of 6 sample sets, 4, 5 and 6-year-old Korea ginseng and 7, 8 and 9-year-old China ginseng, respectively. Each sample set has 20 sample. Nigrogen content was measured by electronic analysis. NIR reflectance spectra were collected over the 1100 to 2500 nm spectral region with a InfraAlyzer 500C (Bran+Luebbe, Germany) equipped with a halogen lapmp and PbS detector and data were collected every 2 nm data point intervals. The calibration models were carried out by multiple linear regression (MLR) and partial least squares (PLS) analysis using IDAS and SESAME software. Result of electronic analysis, Korean ginseng were different mean value in nitrogen content of China ginseng. Ginseng tend to generally decrease the nitrogen content according as cultivation year is over 6 years. The MLR calibration model with 8 wavelengths using IDAS software accurately predicted nitrogen contents with correlation coefficient (R) and standard error of prediction of 0.985 and 0.855%, respectively. In case of SESAME software, the MLR calibration with 9 wavelength was selected the best calibration, R and SEP were 0.972 and 0.596%, respectively. The PLSR calibration model result in 0.969 of R and 0.630 of RMSEP. This study shows the NIR spectroscopy could be applied to determine the nitrogen content in ginseng radix with high accuracy.

• Analytical Science and Technology
• /
• v.20 no.6
• /
• pp.502-507
• /
• 2007

X-ray diffraction method has been widely used for qualitative and quantitative analysis of a mixture of materials since every crystalline material gives a unique X-ray diffraction pattern independently of others, with the intensity of each pattern proportional to that material's concentration in a mixture. For determination of mixing ratios, extracting source spectra correctly is important and crucial. Based on the source spectra extracted, a regression model with non-negativity constraint is applied for determining mixing ratios. In some mixtures, however, X-ray diffraction spectrum has sharp and narrow peaks, which may result in partial negative source spectrum from independent component analysis. We propose several procedures of extracting non-negative source spectra and determining mixing ratios. The proposed method is validated with experimental data on powder mixtures.

• Food Science of Animal Resources
• /
• v.38 no.1
• /
• pp.180-188
• /
• 2018

The market size of home meal replacement (HMR) products has been gradually growing worldwide, even in Korea. In Korean HMR products, meat is the most important food ingredient compared with rice and vegetables. Therefore, this study aimed to evaluate changes in physiochemical and sensory aspects of beef under different preparation processes. For preparing four treatments, beef eye of round (ER) added with salt and sugar (treatment 1) and that without salt and sugar (treatment 2) were mixed with rice and frozen at $-50^{\circ}C$. Beef ER without salt and sugar was also topped onto the rice and frozen (treatment 3), and that was topped onto the rice and precooled before freezing (treatment 4). Physiochemical analyses included cooking and drip losses, shear force, color, salt soluble protein, and sensory attributes were tested. The results showed significantly higher drip loss and total loss in beef ER samples 1 and 2, which were mixed with rice, compared to beef ER samples 3 and 4, which were not mixed with rice. A significantly higher discoloration was also observed in beef ER samples 1 and 2, compared to that in samples 3 and 4. In the partial least squares regression (PLSR) analysis, beef ER sample 4 (precooled before freezing) was highly related to sensory attributes, such as flavor, overall acceptability, and juiciness, and far from non-preferred shear force. As a result, beef ER in HMR sample 4 was the most preferable to the sensory panel, and it had the most desirable physicochemical analysis outcomes.

• Journal of Biosystems Engineering
• /
• v.36 no.5
• /
• pp.369-376
• /
• 2011

The overall goal of this study was to examine the feasibility of predicting firmness of pear fruit by analyzing laser-induced light backscattering images. Thirty-five image analysis characteristics extracted from the laser-induced light backscattering images were used to build partial least squares regression (PLSR) models for predicting firmness of pear fruit. Experiments were conducted with three sets of pear samples which were in same "Shingo" cultivar, harvested in a same season, but produced in different counties. In every experiments with fruit samples produced in a same county, the correlation coefficients of prediction ($r_p$) and root mean square errors of prediction (RMSEP) of the models were 0.550~0.761 and 4.039~6.154 N, respectively. In an experiment with mixed fruit samples produced in different counties, the $r_p$ and RMSEP of the model were 0.669 and 5.02 N, respectively. The experiment results indicate that the analysis of laser-induced light backscattering images could be a useful tool for predicting firmness of pear fruit nondestructively.