جستجوی مقالات مرتبط با کلیدواژه "طیف سنجی مرئی" در نشریات گروه "صنایع غذایی"

In the present study, visible/shortwave near-infrared reflectance spectroscopy (Vis/SWNIR, 425–950 nm) was used to predict the taste index (SSC/TA) and flesh firmness of fig fruits. Besides, the efficiency of LDA and QDA classifiers in detecting ripe, semi-ripe, and unripe figs was studied based on a combination of pretreatment methods. A total of 167 fig trees were selected for the development and validation of the models. Principal component analysis (PCA) was employed to extract the principal components of the spectra. PLS performance and common spectral data pretreatment methods were evaluated using the residual prediction deviation (RPD), predictive correlation coefficient (rp), and root mean square error of prediction (RMSEP). Moreover, the efficiency of the classifiers and pretreatment methods was evaluated using the mean overall accuracy (%) of the testing samples. The highest mean value of RPDs based on the combined pretreatment method of MA + de-trending was 1.79 for flesh firmness (RMSEP = 1.64, rp = 0.845) and 0.89 for the taste index (RMSEP = 10.09, rp = 0.215).LDA and QDA classifiers had an overall accuracy of 93.33 percent (in no-pretreatment spectral data).

Today, the increasing process of food waste and agricultural products is one of the serious challenges in the most countries, especially in developing countries, so one of the serious policies of governments in the food security is to reduce the waste and maintain the quality of agricultural products. So far, several methods have been used to measure the quality of agricultural products, only some of which are technically and industrially justified. Vis / NIR Spectrophotometry method is one of the methods that has been considered and used in evaluating the qualitative characteristics of agricultural products due to its high speed and accuracy. In this regard, in the present study, visible/near infrared Spectrophotometry was used to measure the qualitative changes and classification of K-Lime samples of lemon during the storage period (10, 20 and 30 days). In order to analyze the qualitative characteristics and classify the data extracted from NIR, the pattern recognition methods including principal component analysis (PCA), linear Discriminant analysis (LDA) and support vector machine (SVM) were used. The results showed that Visible/Near Infrared (Vis/NIR) Spectrophotometry was able to differentiate its lemon samples based on storage time. Although PCA, LDA and SVM methods were able to classify lemon samples with good accuracy according to qualitative characteristics, but LDA and SVM methods with 100% accuracy had better accuracy and fit. Also, according to the results, the quadratic function has been determined and introduced as the best function for constructing classification models by LDA and SVM methods.

Determining the status of egg fertilization plays a major role in determining the quality of eggs and their products. In this regard, in order to achieve greater productivity and production, egg evaluation is considered necessary and important in terms of spermatogenesis. In this regard, spectroscopy was performed in the range of 0.01900 nm from 130 local egg samples in the direction of the main diameter for 3 days during the storage period. Spectrum data from spectrometers, in addition to sample information, include unwanted information and noise.  For this reason, in order to achieve accurate classification models, it is necessary to process spectral data before developing the appropriate model. In this regard, intelligent neural network classification was developed based on reference measurements and information of pre-processed spectra by combining different methods of smoothing, normalizing and increasing spectral separation power to determine the presence of sperm in the egg.  Classification results on day zero, first, second, warehousing with 72.3% accuracy, 73.1%, 75.5%, and detection, 86.31, 87.1%, 76% and sensitivity, respectively: 83 61%, 79.63% and 73.3% were obtained.

Investigation of quality characteristics of food products during different manufacturing stages such as storage, processing and consumption is important to reduce food loss. In recent years many researches have established for developing rapid and non-destructive techniques for quality control. In this study the potential of visible and near infrared spectroscopy (Vis/NIRS) in determining the quality parameters of lime including total soluble solid and acidity in reflection mode was investigated in the wavelength range of 400 to 1000 nm.­ The effects of different pre-processing techniques and spectral treatments, such as standard normal variable transformation (SNV), multiplicative scatter correction (MSC), median filter, Savitzky & Golay and the derivatives were evaluated. The model was developed based on partial least squares (PLS) regression. The correlation coefficient (R2) and root mean square error of prediction (RMSEP) for predictive model of soluble solids content was 0.949, 0.105 °Brix respectively. These parameters of the model predicting acidity was found to be 0.909 and 0.118 respectively. These results showed the high potential of Vis/NIRS and the important role of preprocessing techniques in developing precise models for the prediction of lime internal quality characteristics.