ﺳﺎزﻣﺎن ﭘﮋوهشهای ﻋﻠﻤﯽ و ﺻﻨﻌﺘﯽ اﯾﺮانفناوریهای جدید در صنعت غذا2783-350X14120261023Comparison of physicochemical properties of Iranian and Peruvian quinoa powders and the influence of magnetic field on sprouting behaviorComparison of physicochemical properties of Iranian and Peruvian quinoa powders and the influence of magnetic field on sprouting behavior110159910.22104/ift.2026.1599FASepidehVejdanivahidMSc Student, Department of Food Science and Technology, Faculty of Food Industry, Bu-Ali Sina University, Hamedan, Iran0009-0003-1548-1003FakhreddinSalehiAssociate Professor, Department of Food Science and Technology, Faculty of Food Industry, Bu-Ali Sina University, Hamedan, Iran.0000-0002-6653-860XJournal Article20251008Quinoa seeds have a higher nutritional value than other grains. In this study, the moisture and ash contents, pH, acidity, color indexes, total phenolic content (TPC), and antioxidant capacity (AC) of powder prepared from Iranian and Peruvian quinoa seeds were examined and compared. Also, the impacts of untreated water, magnetized water, and magnetic field on the water absorption and weight gain of quinoa seeds following soaking (1 h at 25°C) and sprouting (72 h at 25°C) periods were studied. There were no significant difference in the diameter and thickness between the two quinoa groups (p>0.05). The average diameter of Iranian and Peruvian quinoa seeds was 1.685 mm and 1.757 mm, respectively, and, the average thickness of Iranian and Peruvian quinoa seeds was 1.020 mm and 1.042 mm, respectively. The moisture content, ash content, acidity, and lightness index of Peruvian quinoa seeds powder (QSP) were noticeably higher than that of Iranian QSP (p<0.05). The pH, and redness index of Iranian QSP were noticeably higher than that of Peruvian QSP (p<0.05). The two powders exhibited similar levels of yellowness, TPC, and AC, showing no significant variation (p>0.05). The application of magnetized water and magnetic field accelerated the growth rate of quinoa seeds. Furthermore, they enhanced the amount of water absorbed by the seeds after soaking, as well as their weight following sprouting.Quinoa seeds have a higher nutritional value than other grains. In this study, the moisture and ash contents, pH, acidity, color indexes, total phenolic content (TPC), and antioxidant capacity (AC) of powder prepared from Iranian and Peruvian quinoa seeds were examined and compared. Also, the impacts of untreated water, magnetized water, and magnetic field on the water absorption and weight gain of quinoa seeds following soaking (1 h at 25°C) and sprouting (72 h at 25°C) periods were studied. There were no significant difference in the diameter and thickness between the two quinoa groups (p>0.05). The average diameter of Iranian and Peruvian quinoa seeds was 1.685 mm and 1.757 mm, respectively, and, the average thickness of Iranian and Peruvian quinoa seeds was 1.020 mm and 1.042 mm, respectively. The moisture content, ash content, acidity, and lightness index of Peruvian quinoa seeds powder (QSP) were noticeably higher than that of Iranian QSP (p<0.05). The pH, and redness index of Iranian QSP were noticeably higher than that of Peruvian QSP (p<0.05). The two powders exhibited similar levels of yellowness, TPC, and AC, showing no significant variation (p>0.05). The application of magnetized water and magnetic field accelerated the growth rate of quinoa seeds. Furthermore, they enhanced the amount of water absorbed by the seeds after soaking, as well as their weight following sprouting.https://jift.irost.ir/article_1599_83e8ef518174e1eb6be4a0778d050c9d.pdfﺳﺎزﻣﺎن ﭘﮋوهشهای ﻋﻠﻤﯽ و ﺻﻨﻌﺘﯽ اﯾﺮانفناوریهای جدید در صنعت غذا2783-350X14120261023Raman spectroscopy combined with chemometric modeling for predicting physicochemical quality and safety attributes of Asgari grapesRaman spectroscopy combined with chemometric modeling for predicting physicochemical quality and safety attributes of Asgari grapes1123165910.22104/ift.2026.8044.2258FANazaninTayyebiDepartment of Food Science & Nutrition, medical school, Gonabad university of medical sciences, Gonabad, IranFatanehHashempourHalal Research Center of IRI, Iran Food and Drug Administration, Ministry of Health and Medical Education, Tehran, Iran0000-0003-2670-0956HassanShojaee-MendDepartment of General Courses, Faculty of Medicine, Gonabad University of Medical Sciences, Gonabad, IranZohrehAbdimoghadamFood safety and hygiene department, school of health, university of medical science, Gonabad, Iran.0000-0003-3204-1475Journal Article20251209Rapid 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.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.https://jift.irost.ir/article_1659_e1696007be4eefb81b1a1d39ce48681b.pdfﺳﺎزﻣﺎن ﭘﮋوهشهای ﻋﻠﻤﯽ و ﺻﻨﻌﺘﯽ اﯾﺮانفناوریهای جدید در صنعت غذا2783-350X14120261023Quantitative Detection of Adulteration in Brown Sumac (Rhus coriaria) Powder Using Hyperspectral Imaging and Machine LearningQuantitative Detection of Adulteration in Brown Sumac (Rhus coriaria) Powder Using Hyperspectral Imaging and Machine Learning2534163810.22104/ift.2026.8058.2259FAEsmatKishani FarahaniDepartment of Electrical and Information Technology, Iranian Research Organization for Science and Technology (IROST)0000-0003-3619-9624SeyedehsamanehShojaeilangariBiomedical Engineering Group, Department of Electrical Engineering and Information Technology, Iranian Research Organization for Science and Technology (IROST)0000-0002-7013-3330AlirezaBasiriDepartment of Chemical Technologies, Iranian Research Organization for Science and Technology (IROST)Journal Article20251214This study explores the potential of hyperspectral imaging (HSI) combined with machine learning for the non-destructive detection of ghoore (unripe grape) adulteration in brown sumac, a medicinally and economically valuable spice vulnerable to quality degradation. Samples with adulteration levels of 5%, 20%, 35%, 50%, and 100% were analyzed. Hyperspectral images were acquired and processed using spatial segmentation and Savitzky–Golay filtering to extract informative spectral features. Classification models including Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN) were employed for both binary (pure vs. adulterated) and six-class (specific adulteration levels) classification. The SVM model achieved the highest accuracy, with 99.00% for binary classification and 94.55% for six-class classification. Key discriminative features identified through RF and XGBoost analysis included phase-related components, fractal dimension, and the area under the curve (AUC) in the 700–900 nm spectral range. The results demonstrate that the integration of HSI and machine learning enables a rapid, non-destructive, and reliable method for detecting sumac adulteration, offering significant potential for food quality assurance applications.This study explores the potential of hyperspectral imaging (HSI) combined with machine learning for the non-destructive detection of ghoore (unripe grape) adulteration in brown sumac, a medicinally and economically valuable spice vulnerable to quality degradation. Samples with adulteration levels of 5%, 20%, 35%, 50%, and 100% were analyzed. Hyperspectral images were acquired and processed using spatial segmentation and Savitzky–Golay filtering to extract informative spectral features. Classification models including Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN) were employed for both binary (pure vs. adulterated) and six-class (specific adulteration levels) classification. The SVM model achieved the highest accuracy, with 99.00% for binary classification and 94.55% for six-class classification. Key discriminative features identified through RF and XGBoost analysis included phase-related components, fractal dimension, and the area under the curve (AUC) in the 700–900 nm spectral range. The results demonstrate that the integration of HSI and machine learning enables a rapid, non-destructive, and reliable method for detecting sumac adulteration, offering significant potential for food quality assurance applications.https://jift.irost.ir/article_1638_7e230522657ecdc50e4249581b861f8e.pdfﺳﺎزﻣﺎن ﭘﮋوهشهای ﻋﻠﻤﯽ و ﺻﻨﻌﺘﯽ اﯾﺮانفناوریهای جدید در صنعت غذا2783-350X14120261023Functional flavored milk fortified with hexosomally encapsulated shrimp-derived astaxanthin: Physicochemical, color, and sensory stabilityFunctional flavored milk fortified with hexosomally encapsulated shrimp-derived astaxanthin: Physicochemical, color, and sensory stability3544166410.22104/ift.2026.8143.2274FAFatemehMirzaee KalaeiDepartment of Seafood Processing, Faculty of Marine Sciences, Tarbiat Modares University, Noor, IranSeyed FakhreddinHosseiniDepartment of Seafood Processing, Faculty of Marine Sciences, Tarbiat Modares University, Noor, Iran0000-0003-0565-4276MasoudRezaeiDepartment of Seafood Processing, Faculty of Marine Sciences, Tarbiat Modares University, Noor, IranYadollahYaminiDepartment of Chemistry, Faculty of Sciences, Tarbiat Modares University, Tehran, IranJournal Article20260214This study investigated the production and characterization of a functional flavored milk fortified with shrimp-derived astaxanthin (ASX)-rich oil in free and nanoencapsulated (hexosomal) forms. ASX-rich oil was extracted from green tiger shrimp (Penaeus semisulcatus) processing by-products using supercritical CO₂ extraction and subsequently incorporated into food-grade hexosomes. The resulting ASX-loaded hexosomes exhibited a mean particle size of 164.3 nm, a polydispersity index of 0.29, and a ζ-potential of -36.5 mV. Encapsulation efficiency reached 85.1 ± 3.3%, demonstrating the effective incorporation of ASX-rich oil within the nanostructured lipid carriers. Fortified flavored milk samples were prepared using free and hexosomal ASX-rich oil and evaluated during 12 days of refrigerated storage (4 °C) in terms of physicochemical, rheological, color, and sensory properties. The hexosomal ASX-fortified samples showed pH and titratable acidity profiles comparable to the control, while samples enriched with free ASX exhibited a significantly greater pH decline and higher acidity. Apparent viscosity increased in fortified samples, particularly in those containing hexosomal ASX, reflecting enhanced structural interactions and dispersed phase contributions. Color analysis revealed that nanoencapsulation mitigated excessive increases in redness and yellowness, while maintaining higher lightness compared to free ASX-fortified samples. Sensory evaluation demonstrated that hexosomal encapsulation effectively masked undesirable fishy odor and preserved overall acceptability, whereas free ASX-fortified milk received significantly lower sensory scores. Overall, the results demonstrate that hexosomal nanoencapsulation is an effective strategy to improve the physicochemical stability, color quality, and sensory acceptance of flavored milk fortified with shrimp-derived ASX, highlighting its potential for developing value-added functional dairy products.This study investigated the production and characterization of a functional flavored milk fortified with shrimp-derived astaxanthin (ASX)-rich oil in free and nanoencapsulated (hexosomal) forms. ASX-rich oil was extracted from green tiger shrimp (Penaeus semisulcatus) processing by-products using supercritical CO₂ extraction and subsequently incorporated into food-grade hexosomes. The resulting ASX-loaded hexosomes exhibited a mean particle size of 164.3 nm, a polydispersity index of 0.29, and a ζ-potential of -36.5 mV. Encapsulation efficiency reached 85.1 ± 3.3%, demonstrating the effective incorporation of ASX-rich oil within the nanostructured lipid carriers. Fortified flavored milk samples were prepared using free and hexosomal ASX-rich oil and evaluated during 12 days of refrigerated storage (4 °C) in terms of physicochemical, rheological, color, and sensory properties. The hexosomal ASX-fortified samples showed pH and titratable acidity profiles comparable to the control, while samples enriched with free ASX exhibited a significantly greater pH decline and higher acidity. Apparent viscosity increased in fortified samples, particularly in those containing hexosomal ASX, reflecting enhanced structural interactions and dispersed phase contributions. Color analysis revealed that nanoencapsulation mitigated excessive increases in redness and yellowness, while maintaining higher lightness compared to free ASX-fortified samples. Sensory evaluation demonstrated that hexosomal encapsulation effectively masked undesirable fishy odor and preserved overall acceptability, whereas free ASX-fortified milk received significantly lower sensory scores. Overall, the results demonstrate that hexosomal nanoencapsulation is an effective strategy to improve the physicochemical stability, color quality, and sensory acceptance of flavored milk fortified with shrimp-derived ASX, highlighting its potential for developing value-added functional dairy products.https://jift.irost.ir/article_1664_4f398cb9d6bc79ae567298335b51ba8a.pdfﺳﺎزﻣﺎن ﭘﮋوهشهای ﻋﻠﻤﯽ و ﺻﻨﻌﺘﯽ اﯾﺮانفناوریهای جدید در صنعت غذا2783-350X14120261023Optimizing milling quality of combine-harvested paddy: the impact of pre-drying temperature and intermediate storage durationOptimizing milling quality of combine-harvested paddy: the impact of pre-drying temperature and intermediate storage duration4551163910.22104/ift.2026.8062.2261FAAlirezaAllamehScientific Staff, Rice Research Institute of Iran0000-0003-2224-9010Journal Article20251216Combine harvesting has become widespread in northern Iran, improving efficiency but creating bottlenecks in drying capacity. This often leads to delayed drying and intermediate storage of high-moisture paddy, risking quality deterioration. This study investigated the effects of pre-drying temperature and storage duration on the milling quality of Hashemi rice, a major Iranian variety. Freshly harvested paddy (~20% moisture, w.b.) was pre-dried in a recirculating dryer at 40, 50, or 60 °C to 15% moisture, then stored for 15, 30, or 45 days before undergoing final drying in a conventional batch dryer to 8.5% moisture. A factorial experiment based on a randomized complete block design (RCBD) with five replications was conducted. Results showed that both pre-drying temperature and storage duration significantly affected head rice yield (HRY) and grain fissure percentage (p < 0.01). Increasing the pre-drying temperature from 40 °C to 60 °C reduced HRY by approximately 2% and increased fissures by 3%. Similarly, extending storage from 15 to 45 days reduced HRY by 2.6% and increased fissures by 4.7%. Milling recovery was not significantly affected. These findings highlight that high-temperature pre-drying and prolonged intermediate storage negatively impact grain integrity. To optimize milling quality, it is recommended to use moderate pre-drying temperatures (≤40 °C) and minimize the time between pre- and final drying stages.Combine harvesting has become widespread in northern Iran, improving efficiency but creating bottlenecks in drying capacity. This often leads to delayed drying and intermediate storage of high-moisture paddy, risking quality deterioration. This study investigated the effects of pre-drying temperature and storage duration on the milling quality of Hashemi rice, a major Iranian variety. Freshly harvested paddy (~20% moisture, w.b.) was pre-dried in a recirculating dryer at 40, 50, or 60 °C to 15% moisture, then stored for 15, 30, or 45 days before undergoing final drying in a conventional batch dryer to 8.5% moisture. A factorial experiment based on a randomized complete block design (RCBD) with five replications was conducted. Results showed that both pre-drying temperature and storage duration significantly affected head rice yield (HRY) and grain fissure percentage (p < 0.01). Increasing the pre-drying temperature from 40 °C to 60 °C reduced HRY by approximately 2% and increased fissures by 3%. Similarly, extending storage from 15 to 45 days reduced HRY by 2.6% and increased fissures by 4.7%. Milling recovery was not significantly affected. These findings highlight that high-temperature pre-drying and prolonged intermediate storage negatively impact grain integrity. To optimize milling quality, it is recommended to use moderate pre-drying temperatures (≤40 °C) and minimize the time between pre- and final drying stages.https://jift.irost.ir/article_1639_3c1e4bd67169b8153e0047536c9f541e.pdf