“…It was found that the best prediction results were obtained after 2-Der preprocessing, with accuracies of 90% and 97.5% [30]. NIR spectroscopy and NMR were used to assess macadamia nut extranuclear defects, and the GA-LDA model achieved classification accuracies and specificities of 97.8% and 100%, respectively [31]. The quality of fresh chestnuts was evaluated using NIR diffuse reflectance technology, and the NIR spectra were modeled.…”
Section: Linear Discriminant Analysis After Pca (Pca-da) Modelmentioning
To achieve the rapid identification of Torreya grandis kernels (T. grandis kernels) with different storage times, the near infrared spectra of 300 T. grandis kernels with storage times of 4~9 months were collected. The collected spectral data were modeled, analyzed, and compared using unsupervised and supervised classification methods to determine the optimal rapid identification model for T. grandis kernels with different storage times. The results indicated that principal component analysis (PCA) after derivative processing enabled the visualization of spectral differences and achieved basic detection of samples with different storage times under unsupervised classification. However, it was unable to differentiate samples with storage times of 4~5 and 8~9 months. For supervised classification, the classification accuracy of support vector machine (SVM) modeling was found to be 97.33%. However, it still could not detect the samples with a storage time of 8~9 months. The classification accuracy of linear discriminant analysis after principal component analysis (PCA-DA) was found to be 99.33%, which enabled the detection of T. grandis kernels with different storage times. This research showed that near-infrared spectroscopy technology could be used to achieve the rapid detection of T. grandis kernels with different storage times.
“…It was found that the best prediction results were obtained after 2-Der preprocessing, with accuracies of 90% and 97.5% [30]. NIR spectroscopy and NMR were used to assess macadamia nut extranuclear defects, and the GA-LDA model achieved classification accuracies and specificities of 97.8% and 100%, respectively [31]. The quality of fresh chestnuts was evaluated using NIR diffuse reflectance technology, and the NIR spectra were modeled.…”
Section: Linear Discriminant Analysis After Pca (Pca-da) Modelmentioning
To achieve the rapid identification of Torreya grandis kernels (T. grandis kernels) with different storage times, the near infrared spectra of 300 T. grandis kernels with storage times of 4~9 months were collected. The collected spectral data were modeled, analyzed, and compared using unsupervised and supervised classification methods to determine the optimal rapid identification model for T. grandis kernels with different storage times. The results indicated that principal component analysis (PCA) after derivative processing enabled the visualization of spectral differences and achieved basic detection of samples with different storage times under unsupervised classification. However, it was unable to differentiate samples with storage times of 4~5 and 8~9 months. For supervised classification, the classification accuracy of support vector machine (SVM) modeling was found to be 97.33%. However, it still could not detect the samples with a storage time of 8~9 months. The classification accuracy of linear discriminant analysis after principal component analysis (PCA-DA) was found to be 99.33%, which enabled the detection of T. grandis kernels with different storage times. This research showed that near-infrared spectroscopy technology could be used to achieve the rapid detection of T. grandis kernels with different storage times.
“…11 As chocolate coatings are usually 1- to 2-mm thick, the NIR signal may include some information relating to the composition of the chocolate-coated centre. Notably, NIR spectroscopy has previously been applied to the detection of internal defects in intact macadamia kernels, 12,13 demonstrating that this technique can be used to detect the internal nut composition through the kernel thickness.Figure 1.The appearance of chocolate-coated peanuts and sultanas.…”
Non-destructively identifying the centre composition of panned chocolate goods may be useful in quality assurance settings. However, no studies to date have investigated this topic. In this study, nearinfrared spectra (1000–2500 nm) were collected from chocolate-coated peanuts and chocolate-coated sultanas ( n = 170 of each) in order to investigate the prospect of non-invasively detecting the composition of the centre. Principal component analysis confirmed that the spectra of these samples were distinct from one another. The partial least squares discriminant analysis (PLS-DA) model showed a high level of separation between chocolate-coated peanuts and sultanas in the training set (R2 = 0.95; RPD = 4.4). Discrimination between peanut and sultana samples from an independent test set was also possible, although with slightly less distinct separation between the sample types. A soft independent modelling by class analogy model was also able to differentiate between the two sample types, albeit with higher levels of misclassification compared to PLS-DA. Incorporating samples from different manufacturers may be useful for improving the broader applicability of the model.
“…The rapid method for analysis of phytic acid was done for the green gram seeds (Pande and Mishra 2015). Several studies showed the analytical capacity of FTNIR was excellent for essential and nonessential amino acids, fatty acids, and microbial contaminations in cereals and oilseeds (Lopes et al 2020;Mishra et al 2018;Carvalho et al 2019).…”
The possibility of rapid estimation of moisture, protein, fat, free fatty acid (FFA), and peroxide value (PV) content in peanut kernel was studied by Fourier transform near-infrared spectroscopy (FTNIR) in the diffuse reflectance mode along with chemometric technic. The moisture, fat and protein of fresh and damaged seeds of peanuts ranging from 3 to 9 %, 45 to 57 % and 23 to 27 % respectively, were used for the calibration model building based on partial least squares (PLS) regression. The peanut samples had major peaks at wavenumbers 53.0853, 4954.98, 4464.03, 4070.85, 74.75.63, 8230.21, and 6178.13 in per cm. First and second derivate mathematical preprocessing was also applied in order to eliminate multiple baseline for different chemical quality parameters of peanut. The FFA had the lowest value of calibration and validation errors (0.579 and 0.738) followed by the protein (0.736 and 0.765). The quality of peanut seeds with lowest root mean square error of cross validation of 0.76 and maximum correlation coefficient (R2) of 96.8 was obtained. The comprehensive results signify that FT-NIR spectroscopy can be used for rapid, non destructive quantification of quality parameters in peanut.
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