Food Control最新文献

Effect of processing conditions and exposure assessment of 5-Hydroxymethylfurfural in caramel colors

IF 5.6 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Food Control

Pub Date : 2025-02-18 DOI: 10.1016/j.foodcont.2025.111239

Amelie Sina Wilde , Mikael Pedersen, Mads Hansen , Nadia Klüver Tronier Mikkelsen , Bina Bhattarai, Arvid Fromberg, Lene Duedahl-Olesen

Caramel colors are an important class of colors used in the food production industry, especially within beverages, in the baking industry and confectionery industry. During the production of caramel colors unwanted contaminants can be formed including 5-(Hydroxymethyl)furfural (5-HMF), 2-acetyl-4(5)-tetrahydroxybutylimidazole (THI), and the isomers 4-methylimidazole (4-MI) and 2-methylimidazole (2-MI), however limited information exists on the relationship between the production parameters and these contaminants directly using industrial production recipes and parameters in a reactor scale production. Here, we present an optimized UPLC-MS/MS methods for determination of 5-HMF, 4-MI, THI, and 2-MI in caramel colors (E150a, c, and d) and we use the methods for the determination of these compounds in samples taken throughout the production of the caramel colors. The results revealed that a high color intensity correlates with a low contamination content of 5-HMF in the caramel color E150a and an exponential decrease in the caramel color E150d. Therefore, the color intensity can be used as an indicator for the contamination level in caramel color. Using the 5-HMF results for an exposure evaluation suggests for the caramel colors with high concentrations that there could be a risk for the consumer, however, could be reduced if optimized manufacturing process would be implemented in the industry. The main food categories contributing to 5-HMF exposure are non-alcoholic beverages, alcoholic beverages, confectionary products, savory sauces and baked products that typically contain caramel color. These insights can inform production practices and regulatory approaches to enhance the safety and quality of caramel colors.

{"title":"Effect of processing conditions and exposure assessment of 5-Hydroxymethylfurfural in caramel colors","authors":"Amelie Sina Wilde , Mikael Pedersen, Mads Hansen , Nadia Klüver Tronier Mikkelsen , Bina Bhattarai, Arvid Fromberg, Lene Duedahl-Olesen","doi":"10.1016/j.foodcont.2025.111239","DOIUrl":"10.1016/j.foodcont.2025.111239","url":null,"abstract":"<div><div>Caramel colors are an important class of colors used in the food production industry, especially within beverages, in the baking industry and confectionery industry. During the production of caramel colors unwanted contaminants can be formed including 5-(Hydroxymethyl)furfural (5-HMF), 2-acetyl-4(5)-tetrahydroxybutylimidazole (THI), and the isomers 4-methylimidazole (4-MI) and 2-methylimidazole (2-MI), however limited information exists on the relationship between the production parameters and these contaminants directly using industrial production recipes and parameters in a reactor scale production. Here, we present an optimized UPLC-MS/MS methods for determination of 5-HMF, 4-MI, THI, and 2-MI in caramel colors (E150a, c, and d) and we use the methods for the determination of these compounds in samples taken throughout the production of the caramel colors. The results revealed that a high color intensity correlates with a low contamination content of 5-HMF in the caramel color E150a and an exponential decrease in the caramel color E150d. Therefore, the color intensity can be used as an indicator for the contamination level in caramel color. Using the 5-HMF results for an exposure evaluation suggests for the caramel colors with high concentrations that there could be a risk for the consumer, however, could be reduced if optimized manufacturing process would be implemented in the industry. The main food categories contributing to 5-HMF exposure are non-alcoholic beverages, alcoholic beverages, confectionary products, savory sauces and baked products that typically contain caramel color. These insights can inform production practices and regulatory approaches to enhance the safety and quality of caramel colors.</div></div>","PeriodicalId":319,"journal":{"name":"Food Control","volume":"174 ","pages":"Article 111239"},"PeriodicalIF":5.6,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143456007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Portable system for cocoa bean quality assessment using multi-output learning and augmentation

IF 5.6 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Food Control

Pub Date : 2025-02-18 DOI: 10.1016/j.foodcont.2025.111234

Kamini G. Panchbhai , Madhusudan G. Lanjewar

Cocoa is an essential raw commodity in worldwide trade and requires extreme quality measurement. Precise measurement of cocoa bean ingredients, notably moisture content (MC) and fat content (FC), is essential for quality control. This paper describes a combined system that reliably uses spectroscopy, spectrum preprocessing, data augmentation, dimension reduction, wavelength selection, and advanced machine learning (ML) models to forecast these critical characteristics. The multi-output ML technique was used for MC and FC prediction. Furthermore, spectral augmentation and wavelength selection strategies were used to improve the effectiveness. The proposed method obtained a coefficient of determination (R²) = 0.992, root mean square error (RMSE) = 0.072, and a ratio of performance to deviation (RPD) = 10.620 for MC prediction, while R² = 0.984, RMSE = 0.093, and RPD = 7.919 for FC prediction. Classification analysis was also performed, and the proposed method obtained an accuracy of 96.0% for MC prediction and 90.0% for FC prediction. Moreover, statistical analysis found a t-statistic of 44.445 and a p-value of 0.001. These findings demonstrate the usefulness of this non-destructive technique, which provides a dependable, efficient, and practical option for detecting the quality of cocoa beans and has tremendous potential for use in quality control operations within the cocoa trade.

{"title":"Portable system for cocoa bean quality assessment using multi-output learning and augmentation","authors":"Kamini G. Panchbhai , Madhusudan G. Lanjewar","doi":"10.1016/j.foodcont.2025.111234","DOIUrl":"10.1016/j.foodcont.2025.111234","url":null,"abstract":"<div><div>Cocoa is an essential raw commodity in worldwide trade and requires extreme quality measurement. Precise measurement of cocoa bean ingredients, notably moisture content (MC) and fat content (FC), is essential for quality control. This paper describes a combined system that reliably uses spectroscopy, spectrum preprocessing, data augmentation, dimension reduction, wavelength selection, and advanced machine learning (ML) models to forecast these critical characteristics. The multi-output ML technique was used for MC and FC prediction. Furthermore, spectral augmentation and wavelength selection strategies were used to improve the effectiveness. The proposed method obtained a coefficient of determination (R<sup>2</sup>) = 0.992, root mean square error (RMSE) = 0.072, and a ratio of performance to deviation (RPD) = 10.620 for MC prediction, while R<sup>2</sup> = 0.984, RMSE = 0.093, and RPD = 7.919 for FC prediction. Classification analysis was also performed, and the proposed method obtained an accuracy of 96.0% for MC prediction and 90.0% for FC prediction. Moreover, statistical analysis found a t-statistic of 44.445 and a p-value of 0.001. These findings demonstrate the usefulness of this non-destructive technique, which provides a dependable, efficient, and practical option for detecting the quality of cocoa beans and has tremendous potential for use in quality control operations within the cocoa trade.</div></div>","PeriodicalId":319,"journal":{"name":"Food Control","volume":"174 ","pages":"Article 111234"},"PeriodicalIF":5.6,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development and characterization of carrageenan-based antibacterial films incorporated with natural melanin pigment from niger seed hulls (Guizotia abyssinica) and their efficacy to enhance the shelf-life of strawberries

IF 5.6 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Food Control

Pub Date : 2025-02-17 DOI: 10.1016/j.foodcont.2025.111235

B.T. Sunil Kumar , Jeevan Prasad Reddy , V. Vanajakshi , Akshay H. Dasalkar , Sudheer Kumar Yannam , Umesh H. Hebbar , Sridevi Annapurna Singh

Enhancing the safety and quality of minimally processed foods is a top priority for consumers and the food industry. There is an urgent need for eco-friendly and sustainable packaging films to protect fresh food products. In this study, we attempted to develop carrageenan-based films incorporated with melanin extract with enhanced physicochemical properties, antibacterial activity, and effectiveness in maintaining the quality of strawberries during storage. The carrageenan-melanin films were produced using a solution casting method, incorporating melanin extracted from niger seed hulls as a bioactive additive. The impact of melanin content on the structural, thermal, water barrier, antioxidant, and antimicrobial properties of the carrageenan-based films was assessed. FTIR analysis validated the successful integration of melanin into the carrageenan matrix. The antibacterial activity of the carrageenan-melanin (30 mg) films was tested against common foodborne pathogens, showing effectiveness against Salmonella enterica (60 mm), Staphylococcus aureus (40 mm), Micrococcus luteus (36.67 mm), Klebsiella oxytoca (40 mm), Escherichia coli (42 mm), and Bacillus cereus (31 mm), outperforming the chloramphenicol control. The carrageenan-melanin film (30 mg) significantly restricted bacterial counts and effectively extended the shelf life of strawberries stored at room temperature (25 °C ± 2) for five days and maintained lower heterotrophic bacterial count subsequently. Precise analysis of volatile organic compounds with a sophisticated electronic nose (E-nose) system, revealed the freshness profile of strawberries during storage. Overall, the carrageenan-melanin film shows great promise as a sustainable packaging material with enhanced antibacterial properties for preserving strawberries, representing an innovative solution for sustainable food packaging.

{"title":"Development and characterization of carrageenan-based antibacterial films incorporated with natural melanin pigment from niger seed hulls (Guizotia abyssinica) and their efficacy to enhance the shelf-life of strawberries","authors":"B.T. Sunil Kumar , Jeevan Prasad Reddy , V. Vanajakshi , Akshay H. Dasalkar , Sudheer Kumar Yannam , Umesh H. Hebbar , Sridevi Annapurna Singh","doi":"10.1016/j.foodcont.2025.111235","DOIUrl":"10.1016/j.foodcont.2025.111235","url":null,"abstract":"<div><div>Enhancing the safety and quality of minimally processed foods is a top priority for consumers and the food industry. There is an urgent need for eco-friendly and sustainable packaging films to protect fresh food products. In this study, we attempted to develop carrageenan-based films incorporated with melanin extract with enhanced physicochemical properties, antibacterial activity, and effectiveness in maintaining the quality of strawberries during storage. The carrageenan-melanin films were produced using a solution casting method, incorporating melanin extracted from niger seed hulls as a bioactive additive. The impact of melanin content on the structural, thermal, water barrier, antioxidant, and antimicrobial properties of the carrageenan-based films was assessed. FTIR analysis validated the successful integration of melanin into the carrageenan matrix. The antibacterial activity of the carrageenan-melanin (30 mg) films was tested against common foodborne pathogens, showing effectiveness against <em>Salmonella enterica</em> (60 mm), <em>Staphylococcus aureus</em> (40 mm), <em>Micrococcus luteus</em> (36.67 mm), <em>Klebsiella oxytoca</em> (40 mm), <em>Escherichia coli</em> (42 mm), and <em>Bacillus cereus</em> (31 mm), outperforming the chloramphenicol control. The carrageenan-melanin film (30 mg) significantly restricted bacterial counts and effectively extended the shelf life of strawberries stored at room temperature (25 °C ± 2) for five days and maintained lower heterotrophic bacterial count subsequently. Precise analysis of volatile organic compounds with a sophisticated electronic nose (E-nose) system, revealed the freshness profile of strawberries during storage. Overall, the carrageenan-melanin film shows great promise as a sustainable packaging material with enhanced antibacterial properties for preserving strawberries, representing an innovative solution for sustainable food packaging.</div></div>","PeriodicalId":319,"journal":{"name":"Food Control","volume":"174 ","pages":"Article 111235"},"PeriodicalIF":5.6,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effect of epsilon-poly-L-lysine treatment on swelling, microbial growth and physicochemical quality of vacuum-packed lotus root

IF 5.6 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Food Control

Pub Date : 2025-02-17 DOI: 10.1016/j.foodcont.2025.111238

Qianlong Shi , Yixuan Wang , Cong Han , Xingfeng Guo , Maorun Fu , Subo Tian , Xiaofei Xin

Vacuum-packed lotus roots are prone to swelling and quality deterioration when stored at temperatures above 15 °C. Our study aimed to investigate the impacts of ε-poly-L-lysine (ε-PL) application (0.35 and 0.7 g/L) on swelling, microbial proliferation and quality characteristics of vacuum-packed lotus roots stored at 25 °C for 15 d. Through 16S rDNA amplicon sequencing, the primary bacterial genera responsible for bag swelling were identified as Enterobacter, Klebsiella, and Pantoea. The treatment with 0.7 g/L ε-PL exhibited the strongest inhibitory effect on these genera, effectively preventing swelling. Additionally, ε-PL treatment inhibited browning, suppressed the increase in total acid content, and maintained the levels of soluble sugars, total phenolics, and ascorbic acid. ε-PL assisted in retaining the flavor profile of lotus roots. These findings suggested that ε-PL was effective in preventing swelling, inhibiting microbial growth, and maintaining storage quality in vacuum-packed lotus roots.

{"title":"Effect of epsilon-poly-L-lysine treatment on swelling, microbial growth and physicochemical quality of vacuum-packed lotus root","authors":"Qianlong Shi , Yixuan Wang , Cong Han , Xingfeng Guo , Maorun Fu , Subo Tian , Xiaofei Xin","doi":"10.1016/j.foodcont.2025.111238","DOIUrl":"10.1016/j.foodcont.2025.111238","url":null,"abstract":"<div><div>Vacuum-packed lotus roots are prone to swelling and quality deterioration when stored at temperatures above 15 °C. Our study aimed to investigate the impacts of ε-poly-L-lysine (ε-PL) application (0.35 and 0.7 g/L) on swelling, microbial proliferation and quality characteristics of vacuum-packed lotus roots stored at 25 °C for 15 d. Through 16S rDNA amplicon sequencing, the primary bacterial genera responsible for bag swelling were identified as <em>Enterobacter</em>, <em>Klebsiella</em>, and <em>Pantoea</em>. The treatment with 0.7 g/L ε-PL exhibited the strongest inhibitory effect on these genera, effectively preventing swelling. Additionally, ε-PL treatment inhibited browning, suppressed the increase in total acid content, and maintained the levels of soluble sugars, total phenolics, and ascorbic acid. ε-PL assisted in retaining the flavor profile of lotus roots. These findings suggested that ε-PL was effective in preventing swelling, inhibiting microbial growth, and maintaining storage quality in vacuum-packed lotus roots.</div></div>","PeriodicalId":319,"journal":{"name":"Food Control","volume":"173 ","pages":"Article 111238"},"PeriodicalIF":5.6,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting cadmium accumulation in carrot (Daucus carota L.) using reflectance spectroscopy and machine learning

IF 5.6 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Food Control

Pub Date : 2025-02-15 DOI: 10.1016/j.foodcont.2025.111226

Ninon Maugeais, Guillaume Lassalle

Agricultural commodities such as root vegetables are subject to strict regulations regarding Trace Metal Elements (TME) for food safety reasons. Assessing the compliance of these commodities with authorized limits is usually achieved through costly and time-demanding traditional analytical techniques. As an alternative, we propose a new, rapid-and-scalable approach based on reflectance spectroscopy to assess TME content in root vegetables. The latter relies on exploiting the reflectance spectra of root samples to predict either the absolute concentration of TMEs or their compliance with a certain threshold using machine learning regression and classification. Our approach was successfully applied to predicting cadmium accumulation in the roots of two carrot varieties under controlled conditions, achieving up to 95% accuracy by exploiting the reflectance of root cross-sections (R² = 0.95 and F1-score ≥0.96 in regression and classification, respectively). We also explored non-destructive models using either carrot leaves or unpeeled roots, which achieved moderate to high accuracy for the prediction of root cadmium, respectively (0.48 < R² < 0.87 and 64 < F1-score <90). Our models showed consistent accuracy against varying cadmium limits and allowed identifying wavelengths in the visible and short-wave infrared regions of the spectrum as main contributors of predictions. Our study thus opens encouraging perspectives to assess TME compliance in agricultural commodities, from the field to harvest.

{"title":"Predicting cadmium accumulation in carrot (Daucus carota L.) using reflectance spectroscopy and machine learning","authors":"Ninon Maugeais, Guillaume Lassalle","doi":"10.1016/j.foodcont.2025.111226","DOIUrl":"10.1016/j.foodcont.2025.111226","url":null,"abstract":"<div><div>Agricultural commodities such as root vegetables are subject to strict regulations regarding Trace Metal Elements (TME) for food safety reasons. Assessing the compliance of these commodities with authorized limits is usually achieved through costly and time-demanding traditional analytical techniques. As an alternative, we propose a new, rapid-and-scalable approach based on reflectance spectroscopy to assess TME content in root vegetables. The latter relies on exploiting the reflectance spectra of root samples to predict either the absolute concentration of TMEs or their compliance with a certain threshold using machine learning regression and classification. Our approach was successfully applied to predicting cadmium accumulation in the roots of two carrot varieties under controlled conditions, achieving up to 95% accuracy by exploiting the reflectance of root cross-sections (<em>R</em><sup>2</sup> = 0.95 and F1-score ≥0.96 in regression and classification, respectively). We also explored non-destructive models using either carrot leaves or unpeeled roots, which achieved moderate to high accuracy for the prediction of root cadmium, respectively (0.48 < <em>R</em><sup>2</sup> < 0.87 and 64 < F1-score <90). Our models showed consistent accuracy against varying cadmium limits and allowed identifying wavelengths in the visible and short-wave infrared regions of the spectrum as main contributors of predictions. Our study thus opens encouraging perspectives to assess TME compliance in agricultural commodities, from the field to harvest.</div></div>","PeriodicalId":319,"journal":{"name":"Food Control","volume":"173 ","pages":"Article 111226"},"PeriodicalIF":5.6,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Non-destructive pre-incubation sex determination in chicken eggs using hyperspectral imaging and machine learning

IF 5.6 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Food Control

Pub Date : 2025-02-15 DOI: 10.1016/j.foodcont.2025.111233

Md Wadud Ahmed , Asher Sprigler , Jason Lee Emmert , Mohammed Kamruzzaman

Non-destructive sex determination in eggs can enhance animal welfare, improve economic efficiency, reduce environmental impact, and foster technological innovation in sustainable hatchery operations. This study investigates the effectiveness of non-destructive hyperspectral imaging (HSI) and machine learning for pre-incubation sex prediction in chicken eggs. Multiple classification models such as partial least squares discriminant analysis (PLS-DA), Extreme Gradient Boosting (XGBoost), random forest (RF), and Categorical Boosting (CatBoost) were developed across full wavelengths (452–899 nm) and evaluated through external validation. Multiple spectral pre-processing, such as standard normal variate (SNV), multiplicative scatter correction (MSC), and Savitzky-Golay (SG) were assessed for calibration model development. Further, important feature selection and model optimization techniques were evaluated for robust prediction model development. Using 35 important features, the CatBoost model with SG pre-processed spectra achieved the best performance, with an accuracy of 82.9% on the calibration set and 75.5% on the validation set. The study demonstrated the potential of HSI and advanced machine learning to revolutionize sex prediction in chicken eggs before incubation, offering a non-invasive, precise, and efficient solution for the next-generation poultry industry.

{"title":"Non-destructive pre-incubation sex determination in chicken eggs using hyperspectral imaging and machine learning","authors":"Md Wadud Ahmed , Asher Sprigler , Jason Lee Emmert , Mohammed Kamruzzaman","doi":"10.1016/j.foodcont.2025.111233","DOIUrl":"10.1016/j.foodcont.2025.111233","url":null,"abstract":"<div><div>Non-destructive sex determination in eggs can enhance animal welfare, improve economic efficiency, reduce environmental impact, and foster technological innovation in sustainable hatchery operations. This study investigates the effectiveness of non-destructive hyperspectral imaging (HSI) and machine learning for pre-incubation sex prediction in chicken eggs. Multiple classification models such as partial least squares discriminant analysis (PLS-DA), Extreme Gradient Boosting (XGBoost), random forest (RF), and Categorical Boosting (CatBoost) were developed across full wavelengths (452–899 nm) and evaluated through external validation. Multiple spectral pre-processing, such as standard normal variate (SNV), multiplicative scatter correction (MSC), and Savitzky-Golay (SG) were assessed for calibration model development. Further, important feature selection and model optimization techniques were evaluated for robust prediction model development. Using 35 important features, the CatBoost model with SG pre-processed spectra achieved the best performance, with an accuracy of 82.9% on the calibration set and 75.5% on the validation set. The study demonstrated the potential of HSI and advanced machine learning to revolutionize sex prediction in chicken eggs before incubation, offering a non-invasive, precise, and efficient solution for the next-generation poultry industry.</div></div>","PeriodicalId":319,"journal":{"name":"Food Control","volume":"173 ","pages":"Article 111233"},"PeriodicalIF":5.6,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stepwise strategy based on untargeted metabolomic 1H NMR fingerprinting and pattern recognition for the geographical authentication of virgin olive oils

IF 5.6 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Food Control

Pub Date : 2025-02-13 DOI: 10.1016/j.foodcont.2025.111216

Rosa María Alonso-Salces , Gabriela Elena Viacava , Alba Tres , Stefania Vichi , Enrico Valli , Alessandra Bendini , Tullia Gallina Toschi , Blanca Gallo , Luis Ángel Berrueta , Károly Héberger

¹H NMR fingerprinting of virgin olive oils (VOOs) and a collection of binary classification models arranged in a decision tree are presented as a stepwise strategy to determine the geographical origin of a VOO at four levels, i.e. provenance from an EU member state or outside the EU, country and region of origin, and compliance with a geographical indication scheme. This approach supports current EU regulation that makes labelling of the geographical origin mandatory for olive oil. Currently, official methods for its control are still lacking. Partial least squares discriminant analysis (PLS-DA) and random forest for classification afforded robust and stable binary classification models to verify the geographical origin of VOOs; however, the former outperformed the latter in terms of accuracy and robustness. The prediction abilities of the best binary PLS-DA model for each case study were between 80% and 100% for both classes in cross-validation and in external validation. The satisfactory results achieved for the verification of the geographical origin of VOOs, together with those of our previous studies on the discrimination of olive oil categories, the detection of olive oils blended with vegetable oils (Alonso-Salces et al., 2022), and the determination of the stability, freshness, storage time and conditions, and olive oil best−before date (Alonso-Salces et al., 2021), confirm that a single ¹H NMR analysis of an olive oil sample can provide useful information to control several EU regulations related to olive oil marketing standards (Regulation (EU) 2022/2104 and Regulation (EU) 2024/1143).

{"title":"Stepwise strategy based on untargeted metabolomic 1H NMR fingerprinting and pattern recognition for the geographical authentication of virgin olive oils","authors":"Rosa María Alonso-Salces , Gabriela Elena Viacava , Alba Tres , Stefania Vichi , Enrico Valli , Alessandra Bendini , Tullia Gallina Toschi , Blanca Gallo , Luis Ángel Berrueta , Károly Héberger","doi":"10.1016/j.foodcont.2025.111216","DOIUrl":"10.1016/j.foodcont.2025.111216","url":null,"abstract":"<div><div><sup>1</sup>H NMR fingerprinting of virgin olive oils (VOOs) and a collection of binary classification models arranged in a decision tree are presented as a stepwise strategy to determine the geographical origin of a VOO at four levels, i.e. provenance from an EU member state or outside the EU, country and region of origin, and compliance with a geographical indication scheme. This approach supports current EU regulation that makes labelling of the geographical origin mandatory for olive oil. Currently, official methods for its control are still lacking. Partial least squares discriminant analysis (PLS-DA) and random forest for classification afforded robust and stable binary classification models to verify the geographical origin of VOOs; however, the former outperformed the latter in terms of accuracy and robustness. The prediction abilities of the best binary PLS-DA model for each case study were between 80% and 100% for both classes in cross-validation and in external validation. The satisfactory results achieved for the verification of the geographical origin of VOOs, together with those of our previous studies on the discrimination of olive oil categories, the detection of olive oils blended with vegetable oils (Alonso-Salces et al., 2022), and the determination of the stability, freshness, storage time and conditions, and olive oil best−before date (Alonso-Salces et al., 2021), confirm that a single <sup>1</sup>H NMR analysis of an olive oil sample can provide useful information to control several EU regulations related to olive oil marketing standards (Regulation (EU) 2022/2104 and Regulation (EU) 2024/1143).</div></div>","PeriodicalId":319,"journal":{"name":"Food Control","volume":"173 ","pages":"Article 111216"},"PeriodicalIF":5.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Combining deep convolutional generative adversarial networks with visible-near infrared hyperspectral reflectance to improve prediction accuracy of anthocyanin content in rice seeds

IF 5.6 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Food Control

Pub Date : 2025-02-13 DOI: 10.1016/j.foodcont.2025.111218

Xingsheng Bao , Deyao Huang , Biyun Yang , Jiayi Li , Atoba Tolulope Opeyemi , Renye Wu , Haiyong weng , Zuxin Cheng

Anthocyanin is a crucial reference indicator for evaluating the quality of rice varieties, making it significant to rapidly establish a non-destructive detection method for anthocyanin in rice grains. This study constructs a 1D-DCGAN (One-dimensional deep convolutional generative adversarial network) strategy optimized for one dimensional spectral data and a 1D-CNN (One-dimensional convolutional neural network) model, achieving high-quality generated sample effects and more accurate anthocyanin predictions within a limited dataset. The SG (Savitzky-Golay)-1D-CNN significantly outperforms LSR (Least squares regression), SVM (Support vector machine) and BPNN (Backpropagation neural network) in the test set, with R² (Determination coefficient), RMSE (Root mean square error) and RPD (Residual predictive deviation) values of 0.83, 10.99, and 2.45, respectively. Furthermore, using DCGAN-generated samples to train the SG-1D-CNN by adding a certain number of generated samples can enhance the model's performance in the test set. When the number of added samples is 60 (75% of the original training set sample size), the SG-DCGAN-1D-CNN (Savitzky-Golay deep convolutional generative adversarial network one dimensional convolutional neural network) exhibits the best performance, with R², RMSE, and RPD reaching 0.87, 9.40, and 2.88, respectively. The DCGAN-1D-CNN (Deep convolutional generative adversarial network one dimensional convolutional neural network) method based on this strategy is expected to provide new insights into precise prediction for multi-variety rice seeds.

{"title":"Combining deep convolutional generative adversarial networks with visible-near infrared hyperspectral reflectance to improve prediction accuracy of anthocyanin content in rice seeds","authors":"Xingsheng Bao , Deyao Huang , Biyun Yang , Jiayi Li , Atoba Tolulope Opeyemi , Renye Wu , Haiyong weng , Zuxin Cheng","doi":"10.1016/j.foodcont.2025.111218","DOIUrl":"10.1016/j.foodcont.2025.111218","url":null,"abstract":"<div><div>Anthocyanin is a crucial reference indicator for evaluating the quality of rice varieties, making it significant to rapidly establish a non-destructive detection method for anthocyanin in rice grains. This study constructs a 1D-DCGAN (One-dimensional deep convolutional generative adversarial network) strategy optimized for one dimensional spectral data and a 1D-CNN (One-dimensional convolutional neural network) model, achieving high-quality generated sample effects and more accurate anthocyanin predictions within a limited dataset. The <span>SG</span> (Savitzky-Golay)-1D-CNN significantly outperforms LSR (Least squares regression), SVM (Support vector machine) and BPNN (Backpropagation neural network) in the test set, with R<sup>2</sup> (Determination coefficient), RMSE (Root mean square error) and RPD (Residual predictive deviation) values of 0.83, 10.99, and 2.45, respectively. Furthermore, using DCGAN-generated samples to train the SG-1D-CNN by adding a certain number of generated samples can enhance the model's performance in the test set. When the number of added samples is 60 (75% of the original training set sample size), the SG-DCGAN-1D-CNN (Savitzky-Golay deep convolutional generative adversarial network one dimensional convolutional neural network) exhibits the best performance, with R<sup>2</sup>, RMSE, and RPD reaching 0.87, 9.40, and 2.88, respectively. The DCGAN-1D-CNN (Deep convolutional generative adversarial network one dimensional convolutional neural network) method based on this strategy is expected to provide new insights into precise prediction for multi-variety rice seeds.</div></div>","PeriodicalId":319,"journal":{"name":"Food Control","volume":"174 ","pages":"Article 111218"},"PeriodicalIF":5.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Thermal stability of deoxynivalenol, zearalenone, and their modified forms during baking in oat biscuits

IF 5.6 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Food Control

Pub Date : 2025-02-13 DOI: 10.1016/j.foodcont.2025.111223

Irene Teixido-Orries, Francisco Molino, Ángel Aragonés-Millán, Antonio J. Ramos, Sonia Marín

The present study aimed to investigate the effects of the baking process on some Fusarium mycotoxins (deoxynivalenol (DON), zearalenone (ZEN), T-2 and HT-2 toxins) and their modified forms (deoxynivalenol-3-glucoside (DON-3G), 15-acetyldeoxynivalenol (15-ADON), 3-acetyldeoxynivalenol (3-ADON), deepoxy-deoxynivalenol (DOM-1), α-zearalenol (α-ZEL), and β-zearalenol (β-ZEL)) in oat biscuits. Their content was analysed using HPLC-DAD and UHPLC-MS/MS to evaluate the impact of temperature, time and initial mycotoxin concentration. Also, metabolite screening (sulphated ZEN metabolites, isoDON and norDONs) was performed to provide new insights into the baking effect on mycotoxins. Results indicated that mycotoxin reduction depended significantly on baking temperature and duration. ZEN exhibited higher thermostability than DON-3G, and DON-3G was more thermostable than DON. Under harsh conditions, 15-ADON decreased while DOM-1 increased. isoDON and norDONs emerged during baking. Initial baking phases showed increased levels of ZEN, α-zearalenol-14-S (α-ZEL-14-S) and β-zearalenol-14-S (β-ZEL-14-S) due to the release of hidden mycotoxins, raising safety concerns. T-2 and HT-2 toxins were not found in any oat-based product. The final edible biscuits for each temperature exhibited similar DON and DON-3G concentrations, with higher ZEN levels than initially. Degradation kinetic analysis revealed zero-order kinetics for DON and DON-3G and first-order kinetics for ZEN, offering a predictive tool for mycotoxin levels in biscuits.

{"title":"Thermal stability of deoxynivalenol, zearalenone, and their modified forms during baking in oat biscuits","authors":"Irene Teixido-Orries, Francisco Molino, Ángel Aragonés-Millán, Antonio J. Ramos, Sonia Marín","doi":"10.1016/j.foodcont.2025.111223","DOIUrl":"10.1016/j.foodcont.2025.111223","url":null,"abstract":"<div><div>The present study aimed to investigate the effects of the baking process on some <em>Fusarium</em> mycotoxins (deoxynivalenol (DON), zearalenone (ZEN), T-2 and HT-2 toxins) and their modified forms (deoxynivalenol-3-glucoside (DON-3G), 15-acetyldeoxynivalenol (15-ADON), 3-acetyldeoxynivalenol (3-ADON), deepoxy-deoxynivalenol (DOM-1), α-zearalenol (α-ZEL), and β-zearalenol (β-ZEL)) in oat biscuits. Their content was analysed using HPLC-DAD and UHPLC-MS/MS to evaluate the impact of temperature, time and initial mycotoxin concentration. Also, metabolite screening (sulphated ZEN metabolites, isoDON and norDONs) was performed to provide new insights into the baking effect on mycotoxins. Results indicated that mycotoxin reduction depended significantly on baking temperature and duration. ZEN exhibited higher thermostability than DON-3G, and DON-3G was more thermostable than DON. Under harsh conditions, 15-ADON decreased while DOM-1 increased. isoDON and norDONs emerged during baking. Initial baking phases showed increased levels of ZEN, α-zearalenol-14-S (α-ZEL-14-S) and β-zearalenol-14-S (β-ZEL-14-S) due to the release of hidden mycotoxins, raising safety concerns. T-2 and HT-2 toxins were not found in any oat-based product. The final edible biscuits for each temperature exhibited similar DON and DON-3G concentrations, with higher ZEN levels than initially. Degradation kinetic analysis revealed zero-order kinetics for DON and DON-3G and first-order kinetics for ZEN, offering a predictive tool for mycotoxin levels in biscuits.</div></div>","PeriodicalId":319,"journal":{"name":"Food Control","volume":"173 ","pages":"Article 111223"},"PeriodicalIF":5.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Direct visual detection for methicillin-resistant Staphylococcus aureus in milk based on the RPA-Cas12a-LFS method

IF 5.6 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Food Control

Pub Date : 2025-02-13 DOI: 10.1016/j.foodcont.2025.111209

Yixiang Sun , Liren Zhang , Huimin Wu , Hongjun Chen , Huijie Hu , Chongyu Zhang , Xiaoqiang Li , Yuan Li , Yimai Wang , Liqiang Luo , Yizhi Song

Methicillin-resistant Staphylococcus aureus (MRSA) has emerged as a significant pathogen of global concern, presenting substantial public health risks through foodborne transmission and contributing to elevated morbidity and mortality rates worldwide. The critical need for precise and timely identification of MRSA in food matrices has become increasingly paramount for effective public health protection. While numerous CRISPR-based detection platforms for MRSA have been recently developed, their widespread implementation has been hindered by intricate multi-step protocols and dependency on sophisticated instrumentation. In this study, we developed a direct, accurate and visual detection method for MRSA — the RPA-Cas12a-LFS method. This method comprises two main components: (1) the direct one-pot RPA-Cas12a system, which integrates bacterial lysis, RPA nucleic acid amplification, and CRISPR-Cas12a nucleic acid detection into a single step performed simultaneously at a constant temperature, and (2) the streptavidin-gold nanoparticles (SA-AuNP)-based CRISPR-specific lateral flow strip (LFS). By eliminating the need for nucleic acid extraction, this method significantly simplifies the experimental procedure and reduces the risk of cross-contamination. Through systematic optimization, this method demonstrated exceptional performance, enabling direct and specific identification of MRSA at remarkably low concentrations (10 CFU/mL) within 60 min in various milk samples. This advanced detection method, characterized by its direct sample processing, exceptional accuracy, visual interpretability, cost-efficiency, and minimal equipment requirements, is particularly suitable for on-site and real-time monitoring of pathogenic bacteria in the food industry.

{"title":"Direct visual detection for methicillin-resistant Staphylococcus aureus in milk based on the RPA-Cas12a-LFS method","authors":"Yixiang Sun , Liren Zhang , Huimin Wu , Hongjun Chen , Huijie Hu , Chongyu Zhang , Xiaoqiang Li , Yuan Li , Yimai Wang , Liqiang Luo , Yizhi Song","doi":"10.1016/j.foodcont.2025.111209","DOIUrl":"10.1016/j.foodcont.2025.111209","url":null,"abstract":"<div><div>Methicillin-resistant <em>Staphylococcus aureus</em> (MRSA) has emerged as a significant pathogen of global concern, presenting substantial public health risks through foodborne transmission and contributing to elevated morbidity and mortality rates worldwide. The critical need for precise and timely identification of MRSA in food matrices has become increasingly paramount for effective public health protection. While numerous CRISPR-based detection platforms for MRSA have been recently developed, their widespread implementation has been hindered by intricate multi-step protocols and dependency on sophisticated instrumentation. In this study, we developed a direct, accurate and visual detection method for MRSA — the RPA-Cas12a-LFS method. This method comprises two main components: (1) the direct one-pot RPA-Cas12a system, which integrates bacterial lysis, RPA nucleic acid amplification, and CRISPR-Cas12a nucleic acid detection into a single step performed simultaneously at a constant temperature, and (2) the streptavidin-gold nanoparticles (SA-AuNP)-based CRISPR-specific lateral flow strip (LFS). By eliminating the need for nucleic acid extraction, this method significantly simplifies the experimental procedure and reduces the risk of cross-contamination. Through systematic optimization, this method demonstrated exceptional performance, enabling direct and specific identification of MRSA at remarkably low concentrations (10 CFU/mL) within 60 min in various milk samples. This advanced detection method, characterized by its direct sample processing, exceptional accuracy, visual interpretability, cost-efficiency, and minimal equipment requirements, is particularly suitable for on-site and real-time monitoring of pathogenic bacteria in the food industry.</div></div>","PeriodicalId":319,"journal":{"name":"Food Control","volume":"173 ","pages":"Article 111209"},"PeriodicalIF":5.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0