نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه علوم غذایی و تغذیه، دانشکده پزشکی، دانشگاه علوم پزشکی گناباد، گناباد، ایران
2 مرکز تحقیقات حلال جمهوری اسلامی ایران، سازمان غذا و داروی ایران، وزارت بهداشت، درمان و آموزش پزشکی، تهران، ایران
3 گروه دروس عمومی، دانشکده پزشکی، دانشگاه علوم پزشکی گناباد، گناباد، ایران
4 گروه تغذیه و علوم غذایی، دانشکده پزشکی، دانشگاه علوم پزشکی گناباد، گناباد، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Rapid and low-waste assessment of grape quality remains a challenge for conventional analytical techniques; Raman spectroscopy combined with chemometric modeling provides a promising alternative for predicting the quality attributes of Asgari grapes. Fifty-five grape samples were collected from vineyards across Gonabad, Iran, and key physicochemical parameters—including pH, titratable acidity (TA), flavonoid content, total soluble solids (TSS), anthocyanin content, and the TSS/TA ratio—were measured. Dimensionality reduction was carried out using principal component analysis (PCA) and partial least squares (PLS), followed by the development of predictive models using multiple linear regression (MLR) and support vector machines (SVM). The dataset was randomly divided into training (80%) and testing (20%) subsets. Model performance was evaluated using the coefficient of determination (R²) and the root mean square error of calibration (RMSEC) and prediction (RMSEP). Among the evaluated models, the PCA–MLR hybrid demonstrated the best predictive performance, achieving R² values of 0.75 for anthocyanins, 0.74 for flavonoids, and 0.65 for TSS, with corresponding RMSEC values of 0.56, 0.67, and 1.51, respectively. These results confirm that Raman spectroscopy is a viable and efficient tool for grape quality assessment and represents a practical alternative to conventional chemical analytical methods.
کلیدواژهها [English]
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