Iranian Research Organization for Science and Technology (IROST)Innovative Food Technologies2783-350X13120251023Analyzing cinnamon spice adulteration with spectroscopy: The Influence of data preprocessing on multivariate prediction modelsAnalyzing cinnamon spice adulteration with spectroscopy: The Influence of data preprocessing on multivariate prediction models112156910.22104/ift.2025.7722.2223FAMohammadMasoudiDepartment of Biosystem Engineering, Ferdowsi University of MashhadRasoolKhodabakhshianAssistant Professor
Department of Biosystems Engineering
Ferdowsi University of MashhadMahmood RezaGolzarianSchool of Computer Science and Information Technology,
Murdoch University,
Perth,
AustraliaJournal Article20250703Detecting fraud in the cinnamon supply chain is critical for ensuring consumer safety and maintaining product integrity. Recent advances in spectral data preprocessing techniques offer enhanced accuracy in identifying adulterants in spices like cinnamon. This study investigates the impact of different spectral preprocessing techniques on predicting adulterants—specifically soybean powder, hazelnut shell powder, and dry bread powder—mixed with cinnamon powder using spectroscopy combined with multivariate analysis. The transmittance spectra were collected across the mid-infrared range of 400–4000 cm⁻¹, and Partial Least Squares Regression (PLSR) was employed to model the adulteration levels based on these spectra. Various preprocessing methods were applied to optimize the spectral data. Among them, orthogonal signal correction (OSC) combined with detrending yielded the highest predictive accuracy, with a coefficient of prediction (R²p) ranging from 0.900 to 0.981. Conversely, Extended Multiplicative Scatter Correction (EMSC) and Savitzky-Golay second derivative (D2) were less effective, with R²p values between 0.115 and 0.931. Soybean powder was the easiest adulterant to detect, with a prediction error range of 5–10%. These findings underscore the importance of selecting appropriate preprocessing techniques to improve the accuracy of fraud detection in cinnamon powder using spectroscopic methods.Detecting fraud in the cinnamon supply chain is critical for ensuring consumer safety and maintaining product integrity. Recent advances in spectral data preprocessing techniques offer enhanced accuracy in identifying adulterants in spices like cinnamon. This study investigates the impact of different spectral preprocessing techniques on predicting adulterants—specifically soybean powder, hazelnut shell powder, and dry bread powder—mixed with cinnamon powder using spectroscopy combined with multivariate analysis. The transmittance spectra were collected across the mid-infrared range of 400–4000 cm⁻¹, and Partial Least Squares Regression (PLSR) was employed to model the adulteration levels based on these spectra. Various preprocessing methods were applied to optimize the spectral data. Among them, orthogonal signal correction (OSC) combined with detrending yielded the highest predictive accuracy, with a coefficient of prediction (R²p) ranging from 0.900 to 0.981. Conversely, Extended Multiplicative Scatter Correction (EMSC) and Savitzky-Golay second derivative (D2) were less effective, with R²p values between 0.115 and 0.931. Soybean powder was the easiest adulterant to detect, with a prediction error range of 5–10%. These findings underscore the importance of selecting appropriate preprocessing techniques to improve the accuracy of fraud detection in cinnamon powder using spectroscopic methods.https://jift.irost.ir/article_1569_655ea4bd3b5736d88afc30c9212ccddf.pdfIranian Research Organization for Science and Technology (IROST)Innovative Food Technologies2783-350X13120251023Effect of basil seed gum concentration on the physical, textural and sensory properties of quinoa pancakesEffect of basil seed gum concentration on the physical, textural and sensory properties of quinoa pancakes1322157410.22104/ift.2025.7821.2231FAFakhreddinSalehiAssociate Professor, Department of Food Science and Technology, Faculty of Food Industry, Bu-Ali Sina University, Hamedan, Iran.0000-0002-6653-860XSepidehVejdanivahidMSc Student, Department of Food Science and Technology, Faculty of Food Industry, Bu-Ali Sina University, Hamedan, Iran0009-0003-1548-1003Journal Article20250813Recent studies have highlighted the potential of hydrocolloids to enhance the appearance, texture, and sensory attributes of gluten-free products. Accordingly, the present study aimed to investigate the effect of basil seed gum addition on the quality characteristics of quinoa pancakes. The incorporation of basil seed gum (BSG) at concentrations ranging from 0 to 0.75% significantly influenced the physical, rheological, and sensory properties of quinoa pancakes. Batter viscosity increased with gum addition, rising from 5.0 Pa•s in the control to 19.1 Pa•s at 0.75% BSG (10 RPM), while maintaining shear-thinning behavior. Batter lightness decreased from 73.57 to 69.45, redness shifted towards zero (–3.95 to –1.44), and yellowness increased from 30.71 to 36.28. In baked pancakes, internal lightness increased to 63.11 at 0.75% BSG, while internal yellowness decreased from 39.04 to 34.11. Product moisture content and weight increased significantly, rising from 32.1% to 39.0% and from 11.3% to 13.41%, respectively, while baking loss decreased significantly from 24.5% to 10.6% with increasing levels of gum (p<0.05). Pancake volume ranged from 11.8 to 17.1 cm³, while density declined from 962.3 to 783.7 kg/m³ with rising gum concentrations. Crust hardness increased from 0.20 N in the control to 0.31 N at 0.75% BSG. Sensory evaluation revealed that appearance (8.85), aroma (7.25), flavor (7.65), and overall acceptance (8.20) peaked at 0.25% BSG, whereas texture acceptance reached its highest value (8.90) at 0.75%. Overall, moderate BSG addition (0.25%) increases the sensory acceptance of the product by the consumer and improving product quality.Recent studies have highlighted the potential of hydrocolloids to enhance the appearance, texture, and sensory attributes of gluten-free products. Accordingly, the present study aimed to investigate the effect of basil seed gum addition on the quality characteristics of quinoa pancakes. The incorporation of basil seed gum (BSG) at concentrations ranging from 0 to 0.75% significantly influenced the physical, rheological, and sensory properties of quinoa pancakes. Batter viscosity increased with gum addition, rising from 5.0 Pa•s in the control to 19.1 Pa•s at 0.75% BSG (10 RPM), while maintaining shear-thinning behavior. Batter lightness decreased from 73.57 to 69.45, redness shifted towards zero (–3.95 to –1.44), and yellowness increased from 30.71 to 36.28. In baked pancakes, internal lightness increased to 63.11 at 0.75% BSG, while internal yellowness decreased from 39.04 to 34.11. Product moisture content and weight increased significantly, rising from 32.1% to 39.0% and from 11.3% to 13.41%, respectively, while baking loss decreased significantly from 24.5% to 10.6% with increasing levels of gum (p<0.05). Pancake volume ranged from 11.8 to 17.1 cm³, while density declined from 962.3 to 783.7 kg/m³ with rising gum concentrations. Crust hardness increased from 0.20 N in the control to 0.31 N at 0.75% BSG. Sensory evaluation revealed that appearance (8.85), aroma (7.25), flavor (7.65), and overall acceptance (8.20) peaked at 0.25% BSG, whereas texture acceptance reached its highest value (8.90) at 0.75%. Overall, moderate BSG addition (0.25%) increases the sensory acceptance of the product by the consumer and improving product quality.https://jift.irost.ir/article_1574_0d4f4805c36dc6853edfa4c7e1638b48.pdfIranian Research Organization for Science and Technology (IROST)Innovative Food Technologies2783-350X13120251023Kinetic modeling of physicochemical changes in protein bars fortified with spirulina and phycocyanin during storageKinetic modeling of physicochemical changes in protein bars fortified with spirulina and phycocyanin during storage2335157210.22104/ift.2025.7810.2229FAParnianPourghasemianMaster’s student, Department of Food Science and Technology, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran.َAmirPourfarzadDepartment of Food Technologies,
Institute of Chemical Technologies,
Iranian Research Organization for Science &amp; Technology (IROST),
Azadegan Highway-South, Ahmadabad Mostoufi, Parsa Sq., Enghelab St., Tehran, IranAriaBabakhaniAssociate Professor, Fisheries Department, Faculty of Natural Resources, University of Guilan, Sowmeh Sara, Guilan, Iran.Journal Article20250809Introduction: Nutrient bars have emerged as a practical and appealing solution for individuals with busy lifestyles, offering a convenient alternative to missed meals. With optimal formulation, these products can deliver a balanced composition of essential macro- and micronutrients—especially proteins and carbohydrates—necessary for maintaining normal body functions, thereby playing a significant role in improving the nutritional status of consumers. Among the diverse categories of nutritional bars, protein bars constitute a significant segment of the market. However, ensuring their physicochemical and oxidative stability during storage, particularly under ambient conditions, remains a key challenge for the food industry. Recent research has highlighted the potential of microalgae, such as Spirulina, to enhance the technological and nutritional properties of food products. Spirulina is rich in proteins, essential fatty acids, vitamins, minerals, and various antioxidants, and is widely used as a dietary supplement in diverse nutritional regimens. One of its most important bioactive compounds is phycocyanin, a natural blue protein pigment with potent antioxidant properties. Owing to the nutritional and functional advantages of these compounds, their incorporation into food products has attracted growing interest as a means to enhance both nutritional value and product quality. The present study aims to investigate and model the kinetics of physicochemical and oxidative changes in protein bars enriched with Spirulina and phycocyanin over a 28-day storage period. The parameters examined include moisture content, water activity, titratable acidity, peroxide value, and total color difference (ΔE). Kinetic modeling of physicochemical changes in food systems is a crucial tool for understanding, predicting, and controlling quality behavior during storage. It plays a vital role in shelf life determination, packaging design, optimization of storage conditions, and the overall assessment of product stability.Introduction: Nutrient bars have emerged as a practical and appealing solution for individuals with busy lifestyles, offering a convenient alternative to missed meals. With optimal formulation, these products can deliver a balanced composition of essential macro- and micronutrients—especially proteins and carbohydrates—necessary for maintaining normal body functions, thereby playing a significant role in improving the nutritional status of consumers. Among the diverse categories of nutritional bars, protein bars constitute a significant segment of the market. However, ensuring their physicochemical and oxidative stability during storage, particularly under ambient conditions, remains a key challenge for the food industry. Recent research has highlighted the potential of microalgae, such as Spirulina, to enhance the technological and nutritional properties of food products. Spirulina is rich in proteins, essential fatty acids, vitamins, minerals, and various antioxidants, and is widely used as a dietary supplement in diverse nutritional regimens. One of its most important bioactive compounds is phycocyanin, a natural blue protein pigment with potent antioxidant properties. Owing to the nutritional and functional advantages of these compounds, their incorporation into food products has attracted growing interest as a means to enhance both nutritional value and product quality. The present study aims to investigate and model the kinetics of physicochemical and oxidative changes in protein bars enriched with Spirulina and phycocyanin over a 28-day storage period. The parameters examined include moisture content, water activity, titratable acidity, peroxide value, and total color difference (ΔE). Kinetic modeling of physicochemical changes in food systems is a crucial tool for understanding, predicting, and controlling quality behavior during storage. It plays a vital role in shelf life determination, packaging design, optimization of storage conditions, and the overall assessment of product stability.https://jift.irost.ir/article_1572_452bf208bf901322968557227b8f6efe.pdfIranian Research Organization for Science and Technology (IROST)Innovative Food Technologies2783-350X13120251023Physicochemical and structural properties assessment of rice bran oil and proteinsPhysicochemical and structural properties assessment of rice bran oil and proteins3760157310.22104/ift.2025.7772.2226FAShivaAbbasiPh.D. Candidate, Department of Food and Conversion Industries, Iranian Research Organization for Science and Technology (IROST), Tehran, Iran0000-0002-1539-0537HomaTorabizadehAssociate Professor, Department of Food and Conversion Industries, Iranian Research Organization for Science and Technology (IROST), Tehran, Iran0000-0002-1063-5021Journal Article20250727Rice (Oryza sativa L.) is a staple grain vital for human nutrition, particularly in Asia. Rice bran, constituting approximately 10% of the outer layer of brown rice, is a by-product of the milling process. It contains 10–16% protein, 12–22% lipids, dietary fiber, and various bioactive compounds, including B vitamins, vitamin E, and gamma-oryzanol, which exhibit notable antioxidant and nutritional properties. The oil extracted from rice bran features a well-balanced fatty acid composition, predominantly comprising 43% oleic acid (monounsaturated), 32% linoleic acid (polyunsaturated), and 15% palmitic acid (saturated). This profile contributes significantly to cardiovascular health improvement and oxidative stress reduction. The saturated to unsaturated fatty acid ratio is approximately 20:80, with minor but important amounts of linolenic acid (~0.8%). Non-saponifiable constituents such as tocopherols, phytosterols, polyphenols, and gamma-oryzanol (approximately 1.76% in enzyme-extracted oil) further enhance the oil’s antioxidant capacity and cholesterol-lowering effects. Rice bran proteins, including albumin, globulin, glutelin, and prolamin, demonstrate digestibility rates exceeding 90% and a protein digestibility-corrected amino acid score (PDCAAS) ranging from 2.0 to 2.5, underscoring their value as a high-quality protein source. Genetic variability among rice varieties leads to differences in the amino acid profile of rice bran. The proportion of essential amino acids relative to total amino acids in various bran fractions ranges from 31.35% to 34.8%, indicating consistent protein quality throughout the bran layers. Despite its rich nutritional composition, rice bran direct consumption in human diets remains limited, with most usage directed toward animal feed, fertilizers, and biofuel production. Nonetheless, due to its balanced amino acid content, beneficial fatty acid profile, and potent antioxidant compounds, rice bran holds considerable potential as a functional ingredient in food, pharmaceutical, and cosmetic industries.Rice (Oryza sativa L.) is a staple grain vital for human nutrition, particularly in Asia. Rice bran, constituting approximately 10% of the outer layer of brown rice, is a by-product of the milling process. It contains 10–16% protein, 12–22% lipids, dietary fiber, and various bioactive compounds, including B vitamins, vitamin E, and gamma-oryzanol, which exhibit notable antioxidant and nutritional properties. The oil extracted from rice bran features a well-balanced fatty acid composition, predominantly comprising 43% oleic acid (monounsaturated), 32% linoleic acid (polyunsaturated), and 15% palmitic acid (saturated). This profile contributes significantly to cardiovascular health improvement and oxidative stress reduction. The saturated to unsaturated fatty acid ratio is approximately 20:80, with minor but important amounts of linolenic acid (~0.8%). Non-saponifiable constituents such as tocopherols, phytosterols, polyphenols, and gamma-oryzanol (approximately 1.76% in enzyme-extracted oil) further enhance the oil’s antioxidant capacity and cholesterol-lowering effects. Rice bran proteins, including albumin, globulin, glutelin, and prolamin, demonstrate digestibility rates exceeding 90% and a protein digestibility-corrected amino acid score (PDCAAS) ranging from 2.0 to 2.5, underscoring their value as a high-quality protein source. Genetic variability among rice varieties leads to differences in the amino acid profile of rice bran. The proportion of essential amino acids relative to total amino acids in various bran fractions ranges from 31.35% to 34.8%, indicating consistent protein quality throughout the bran layers. Despite its rich nutritional composition, rice bran direct consumption in human diets remains limited, with most usage directed toward animal feed, fertilizers, and biofuel production. Nonetheless, due to its balanced amino acid content, beneficial fatty acid profile, and potent antioxidant compounds, rice bran holds considerable potential as a functional ingredient in food, pharmaceutical, and cosmetic industries.https://jift.irost.ir/article_1573_a1afc58c6ca9540d057299ec3016d726.pdfIranian Research Organization for Science and Technology (IROST)Innovative Food Technologies2783-350X13120251023Analyzing the performance of deep learning models in tea fraud detection: A case study of black teaAnalyzing the performance of deep learning models in tea fraud detection: A case study of black tea6171157610.22104/ift.2025.7831.2232FASajadSabziDept. of Biosystem Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran;RaziyehPourdarbaniDept. of Biosystem engineering, University of Mohaghegh ArdabiliJournal Article20250820Tea is not only one of the most popular beverages in the world, but also one of the most important agricultural products due to its health benefits and wide applications in various industries. However, tea fraud is one of the main challenges in the industry. These frauds include the addition of foreign materials, colors, or falsification of geographical origin, which have negative effects on the health of consumers. At present paper, two deep learning models namely EfficientNet and Swin Transformer, were tested in detecting three types of fraud (tea waste, low-quality foreign tea, and expired tea) in Iranian black tea. The results indicate that the EfficientNet model was more successful in detecting tea waste than foreign tea and expired tea (with accuracy of 96.8% and F1-Score of 95.2%), while the Swin Transformer model performed better in detecting foreign tea and expired tea, showing an accuracy of 94.5% and an F1-Score of 93.7%, respectively. However, improved settings are suggested to reduce errors and improve the performance of the models.Tea is not only one of the most popular beverages in the world, but also one of the most important agricultural products due to its health benefits and wide applications in various industries. However, tea fraud is one of the main challenges in the industry. These frauds include the addition of foreign materials, colors, or falsification of geographical origin, which have negative effects on the health of consumers. At present paper, two deep learning models namely EfficientNet and Swin Transformer, were tested in detecting three types of fraud (tea waste, low-quality foreign tea, and expired tea) in Iranian black tea. The results indicate that the EfficientNet model was more successful in detecting tea waste than foreign tea and expired tea (with accuracy of 96.8% and F1-Score of 95.2%), while the Swin Transformer model performed better in detecting foreign tea and expired tea, showing an accuracy of 94.5% and an F1-Score of 93.7%, respectively. However, improved settings are suggested to reduce errors and improve the performance of the models.https://jift.irost.ir/article_1576_af5afd7f7c807171981d443ad4f4f648.pdfIranian Research Organization for Science and Technology (IROST)Innovative Food Technologies2783-350X13120251023Analysis of the effects of ultraviolet and infrared lamps on the physical and chemical properties of eggs using principal component analysis MethodAnalysis of the effects of ultraviolet and infrared lamps on the physical and chemical properties of eggs using principal component analysis Method7381158410.22104/ift.2025.7875.2238FAMohammad JavadMahmoodiMaster's degree graduate in Biosystems Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.MohsenAzadbakhtDepartment of Bio-System Mechanical Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.0000-0002-5726-9321BehrouzDastarProfessor of Department of Animal and Poultry Nutrition, Faculty of Animal Science, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, IranAliAsghariResearch Professional, Department of Soils and Agri-Food Engineering, Université Laval, Quebec, CanadaJournal Article20250916Given the critical importance of egg quality for consumer health and the prevention of economic losses in the industry, identifying factors that help maintain or enhance quality is essential. This study aimed to investigate the effects of the number and exposure time of UV and IR lamps on the quality characteristics of eggs using Principal Component Analysis (PCA). A total of 56 intact eggs were collected and subjected to pre-treatments with UV and IR lamps, both with and without sunflower oil coating. Subsequently, quality parameters of the samples were measured, and the resulting data were evaluated using PCA. The PCA results indicated that the type and intensity of UV and IR irradiation had distinct impacts on egg quality attributes. UV exposure produced more diverse patterns, whereas IR exposure resulted in more uniform responses. Quality variables such as volume, density, crude protein, and total ash played the most significant roles in differentiating the treatments. Moreover, prolonged exposure time intensified differences between groups, highlighting PCA as an effective tool for identifying key factors influencing egg quality.Given the critical importance of egg quality for consumer health and the prevention of economic losses in the industry, identifying factors that help maintain or enhance quality is essential. This study aimed to investigate the effects of the number and exposure time of UV and IR lamps on the quality characteristics of eggs using Principal Component Analysis (PCA). A total of 56 intact eggs were collected and subjected to pre-treatments with UV and IR lamps, both with and without sunflower oil coating. Subsequently, quality parameters of the samples were measured, and the resulting data were evaluated using PCA. The PCA results indicated that the type and intensity of UV and IR irradiation had distinct impacts on egg quality attributes. UV exposure produced more diverse patterns, whereas IR exposure resulted in more uniform responses. Quality variables such as volume, density, crude protein, and total ash played the most significant roles in differentiating the treatments. Moreover, prolonged exposure time intensified differences between groups, highlighting PCA as an effective tool for identifying key factors influencing egg quality.https://jift.irost.ir/article_1584_277281aada22045c03945dcb2ca6f2ec.pdf