Pub Date : 2025-02-27DOI: 10.1016/j.foodcont.2025.111198
Ganyu Gu , Bin Zhou , Yishan Yang , Xiangwu Nou , Patricia D. Millner , Boce Zhang , Yaguang Luo
Packaged “ready-to-eat” (RTE) fresh leafy greens grown hydroponically (GH) in controlled environment has becoming an important food choice for consumers. However, a significant data gap exists regarding the microbial profile of these products on the markets. In this study, commercially packaged GH baby spinach products were sampled from different retail stores in the Mid-Atlantic region and were compared with the packaged RTE baby spinach grown from soil-based (GS) open field production available in the same stores. Aerobic bacteria and yeast and mold populations were determined by culture-based method, and microbiome was analyzed via high-throughput 16S rRNA gene and ITS amplicon sequencing. While no major differences in microbial population were seen, distinctive patterns on microbial community between GH and GS products were observed. The dominant microbes on GS baby spinach were bacterial genus Pseudomonas (average relative abundance: 70%) and fungal genus Cystofilobasidium (53%). The most abundant bacteria identified on GH baby spinach was a Cyanobacteria genus Synechocystis (25%), and the major fungal genera were Penicillium (22%) and Cladosporium (15%). The abundance of Pseudomonas in RTE leafy greens is well documented; but there have been no reports regarding Synechocystis presence in RTE baby spinach. Further investigations are warranted to investigate the interactions of the microbiome (bacteria, fungi, and Synechocystis), food safety, and quality of RTE GH products.
{"title":"Microbial profiles of commercially packaged baby spinach from hydroponic controlled environment agriculture and soil-based open field production","authors":"Ganyu Gu , Bin Zhou , Yishan Yang , Xiangwu Nou , Patricia D. Millner , Boce Zhang , Yaguang Luo","doi":"10.1016/j.foodcont.2025.111198","DOIUrl":"10.1016/j.foodcont.2025.111198","url":null,"abstract":"<div><div>Packaged “ready-to-eat” (RTE) fresh leafy greens grown hydroponically (GH) in controlled environment has becoming an important food choice for consumers. However, a significant data gap exists regarding the microbial profile of these products on the markets. In this study, commercially packaged GH baby spinach products were sampled from different retail stores in the Mid-Atlantic region and were compared with the packaged RTE baby spinach grown from soil-based (GS) open field production available in the same stores. Aerobic bacteria and yeast and mold populations were determined by culture-based method, and microbiome was analyzed via high-throughput 16S rRNA gene and ITS amplicon sequencing. While no major differences in microbial population were seen, distinctive patterns on microbial community between GH and GS products were observed. The dominant microbes on GS baby spinach were bacterial genus <em>Pseudomonas</em> (average relative abundance: 70%) and fungal genus <em>Cystofilobasidium</em> (53%). The most abundant bacteria identified on GH baby spinach was a Cyanobacteria genus <em>Synechocystis</em> (25%), and the major fungal genera were <em>Penicillium</em> (22%) and <em>Cladosporium</em> (15%). The abundance of <em>Pseudomonas</em> in RTE leafy greens is well documented; but there have been no reports regarding <em>Synechocystis</em> presence in RTE baby spinach. Further investigations are warranted to investigate the interactions of the microbiome (bacteria, fungi, and <em>Synechocystis</em>), food safety, and quality of RTE GH products.</div></div>","PeriodicalId":319,"journal":{"name":"Food Control","volume":"175 ","pages":"Article 111198"},"PeriodicalIF":5.6,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628438","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}
Pub Date : 2025-02-27DOI: 10.1016/j.foodcont.2025.111255
Chenghong Wang , Zhongjun Yan , Fei Shen , Qiuhui Hu , Xirong Huang
Peanuts, a globally significant crop, are prone to aflatoxin B1 (AFB1) contamination, posing a significant threat to food safety. This study employed laser-induced fluorescence spectroscopy (LIFS) to detect AFB1 in single peanuts. Natural contamination conditions were simulated to obtain peanuts with different AFB1 levels, and surface fluorescence signals were collected using single-probe and three-probe methods. Toxin content was quantified through wet chemistry, and machine learning was applied for classification. The results showed that increasing the number of probes significantly improved detection accuracy and reduced the false negative rate (FNR). A weighted algorithm was proposed to enhance spectral analysis, which can amplify the differences between contaminated and uncontaminated samples. A linear SVM based on the three-probe weighted fluorescence spectral data achieved best discriminant ability (accuracy = 100%). Additionally, the Random Forest (RF) algorithm identified six key wavelengths, enabling an SVM classifier to predict contamination with 94.12% accuracy and a 0% FNR. This high-sensitivity, high-accuracy method provides a reliable technical solution for rapid, nondestructive AFB1 detection in peanuts, offering promise for critical applications in food safety monitoring.
{"title":"Enhanced detection of aflatoxin B1 in single peanut kernels using laser-induced fluorescence and a weighted algorithm","authors":"Chenghong Wang , Zhongjun Yan , Fei Shen , Qiuhui Hu , Xirong Huang","doi":"10.1016/j.foodcont.2025.111255","DOIUrl":"10.1016/j.foodcont.2025.111255","url":null,"abstract":"<div><div>Peanuts, a globally significant crop, are prone to aflatoxin B<sub>1</sub> (AFB<sub>1</sub>) contamination, posing a significant threat to food safety. This study employed laser-induced fluorescence spectroscopy (LIFS) to detect AFB<sub>1</sub> in single peanuts. Natural contamination conditions were simulated to obtain peanuts with different AFB<sub>1</sub> levels, and surface fluorescence signals were collected using single-probe and three-probe methods. Toxin content was quantified through wet chemistry, and machine learning was applied for classification. The results showed that increasing the number of probes significantly improved detection accuracy and reduced the false negative rate (FNR). A weighted algorithm was proposed to enhance spectral analysis, which can amplify the differences between contaminated and uncontaminated samples. A linear SVM based on the three-probe weighted fluorescence spectral data achieved best discriminant ability (accuracy = 100%). Additionally, the Random Forest (RF) algorithm identified six key wavelengths, enabling an SVM classifier to predict contamination with 94.12% accuracy and a 0% FNR. This high-sensitivity, high-accuracy method provides a reliable technical solution for rapid, nondestructive AFB<sub>1</sub> detection in peanuts, offering promise for critical applications in food safety monitoring.</div></div>","PeriodicalId":319,"journal":{"name":"Food Control","volume":"174 ","pages":"Article 111255"},"PeriodicalIF":5.6,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527157","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}
Pub Date : 2025-02-26DOI: 10.1016/j.foodcont.2025.111256
Jingbo Dai , Xiaobin Chen , Yao Zhang , Min Zhang , Yunyuan Dong , Qifu Zheng , Jianming Liao , Ying Zhao
Food safety, particularly the risks posed by pesticide residues, has become a critical public health concern. Existing detection methods are often slow, expensive, and require complex equipment, limiting their widespread use. This study introduces a rapid test strips system for pesticide residues, focusing on cholinesterase and organophosphate pesticides. The system combines a colorimetric reaction with machine vision to automate image analysis. Key image processing techniques, including noise reduction and threshold extraction, are used to analyze RGB values from the test strips. Multicolor feature indices are then derived to process the data. Additionally, an improved genetic programming-symbolic regression (GP-SR) model is developed to establish the relationship between these indices and pesticide residue levels. Experimental results show that the enhanced GP-SR model increases the R2 value by up to 0.195 after normalization, improves the coefficient of determination by 2.5%, and reduces the RMSE by 16%. This approach offers a more efficient and accurate method for detecting pesticide residues in fruits and vegetables, contributing to improved food safety monitoring.
{"title":"Machine learning-enhanced color recognition of test strips for rapid pesticide residue detection in fruits and vegetables","authors":"Jingbo Dai , Xiaobin Chen , Yao Zhang , Min Zhang , Yunyuan Dong , Qifu Zheng , Jianming Liao , Ying Zhao","doi":"10.1016/j.foodcont.2025.111256","DOIUrl":"10.1016/j.foodcont.2025.111256","url":null,"abstract":"<div><div>Food safety, particularly the risks posed by pesticide residues, has become a critical public health concern. Existing detection methods are often slow, expensive, and require complex equipment, limiting their widespread use. This study introduces a rapid test strips system for pesticide residues, focusing on cholinesterase and organophosphate pesticides. The system combines a colorimetric reaction with machine vision to automate image analysis. Key image processing techniques, including noise reduction and threshold extraction, are used to analyze RGB values from the test strips. Multicolor feature indices are then derived to process the data. Additionally, an improved genetic programming-symbolic regression (GP-SR) model is developed to establish the relationship between these indices and pesticide residue levels. Experimental results show that the enhanced GP-SR model increases the R<sup>2</sup> value by up to 0.195 after normalization, improves the coefficient of determination by 2.5%, and reduces the RMSE by 16%. This approach offers a more efficient and accurate method for detecting pesticide residues in fruits and vegetables, contributing to improved food safety monitoring.</div></div>","PeriodicalId":319,"journal":{"name":"Food Control","volume":"174 ","pages":"Article 111256"},"PeriodicalIF":5.6,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548134","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}
Pub Date : 2025-02-25DOI: 10.1016/j.foodcont.2025.111254
D.T. Mugadza , K.W. Feresu , T.Z. Jombo , J.W. Mugombi , S.P. Nyarugwe , S. Chimuti , V. Nyanhete , F.A. Manditsera , L. Macheka
Food safety is a critical global public health issue, with foodborne illnesses identified as one of the leading causes of mortality worldwide. This study provides a comprehensive review of Zimbabwe's food safety control legislative framework system, focusing on its legislative and regulatory framework and compliance with international standards. The research employed qualitative methods, including document analysis and key informant interviews.
The results show that Zimbabwe had a broad food safety framework comprising 15 Acts and 31 regulations overseen by multiple ministries. However, many of these Acts and regulations are outdated and need revision to meet emerging global food safety standards. Additionally, the food safety legislation framework is characterised by fragmentation and oversight by multiple agencies, leading to duplicated efforts, jurisdictional overlaps, and inefficiencies in enforcing standards. The study also highlights challenges faced by the three public food control laboratories where a lack of coordination and centralised data sharing impedes effective regulatory oversight. The findings reveal the urgent need for regulatory updates, improved inter-agency collaboration, and alignment with international standards to enhance food safety and protect public health. Recommendations include harmonising Zimbabwe's food control system under a centralised framework to promote collaboration among agencies, eliminate redundancies, and create a cohesive approach to food safety management.
{"title":"Food safety governance in Zimbabwe: Challenges, regulatory gaps, and strategies for global compliance","authors":"D.T. Mugadza , K.W. Feresu , T.Z. Jombo , J.W. Mugombi , S.P. Nyarugwe , S. Chimuti , V. Nyanhete , F.A. Manditsera , L. Macheka","doi":"10.1016/j.foodcont.2025.111254","DOIUrl":"10.1016/j.foodcont.2025.111254","url":null,"abstract":"<div><div>Food safety is a critical global public health issue, with foodborne illnesses identified as one of the leading causes of mortality worldwide. This study provides a comprehensive review of Zimbabwe's food safety control legislative framework system, focusing on its legislative and regulatory framework and compliance with international standards. The research employed qualitative methods, including document analysis and key informant interviews.</div><div>The results show that Zimbabwe had a broad food safety framework comprising 15 Acts and 31 regulations overseen by multiple ministries. However, many of these Acts and regulations are outdated and need revision to meet emerging global food safety standards. Additionally, the food safety legislation framework is characterised by fragmentation and oversight by multiple agencies, leading to duplicated efforts, jurisdictional overlaps, and inefficiencies in enforcing standards. The study also highlights challenges faced by the three public food control laboratories where a lack of coordination and centralised data sharing impedes effective regulatory oversight. The findings reveal the urgent need for regulatory updates, improved inter-agency collaboration, and alignment with international standards to enhance food safety and protect public health. Recommendations include harmonising Zimbabwe's food control system under a centralised framework to promote collaboration among agencies, eliminate redundancies, and create a cohesive approach to food safety management.</div></div>","PeriodicalId":319,"journal":{"name":"Food Control","volume":"174 ","pages":"Article 111254"},"PeriodicalIF":5.6,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519348","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}
Pub Date : 2025-02-25DOI: 10.1016/j.foodcont.2025.111252
Kabiru Ayobami Jimoh , Norhashila Hashim , Rosnah Shamsudin , Hasfalina Che Man , Mahirah Jahari
Hyperspectral imaging (HSI) technology combined with chemometrics has offered a profound advancement in rice quality assessment. With the advent of this technology, glutinous rice quality such as moisture content, colour indices, protein, fat, and ash content are swiftly and accurately predicted without destroying the grains. The technology eliminates the laborious, time consuming, chemically demanding and expensive traditional method of grain quality determination. However, the complexity of HSI technology makes it more prominent in the research field because it requires high technical skills. Therefore, the development of a smart user interface (GUI) called HyperspecGlu in this study aids the rapid and nondestructive application of HSI data coupled with chemometrics for the determination of glutinous rice quality which includes colour change, golden index, moisture, protein, fat and ash content. The tool simplifies the HSI data processing and glutinous rice quality prediction, featuring data upload, preprocessing, model execution and result visualization through a click-and-run button. Employing three-stage processing techniques which include Savitzky-Golay first derivative techniques for spectral correction, redundant wavelength removal using variable importance space shrinkage approach and predictive model development gave a good prediction accuracy, which makes the HyperspecGlu reliable. Therefore, the HyperspecGlu toolbox is capable of swiftly detecting glutinous rice quality with high accuracy based on the HSI combined with chemometrics and the GUI makes the process available and accessible for users with little or no programming knowledge.
{"title":"Development of near-infrared hyperspectral-based smart interface for glutinous rice quality detection","authors":"Kabiru Ayobami Jimoh , Norhashila Hashim , Rosnah Shamsudin , Hasfalina Che Man , Mahirah Jahari","doi":"10.1016/j.foodcont.2025.111252","DOIUrl":"10.1016/j.foodcont.2025.111252","url":null,"abstract":"<div><div>Hyperspectral imaging (HSI) technology combined with chemometrics has offered a profound advancement in rice quality assessment. With the advent of this technology, glutinous rice quality such as moisture content, colour indices, protein, fat, and ash content are swiftly and accurately predicted without destroying the grains. The technology eliminates the laborious, time consuming, chemically demanding and expensive traditional method of grain quality determination. However, the complexity of HSI technology makes it more prominent in the research field because it requires high technical skills. Therefore, the development of a smart user interface (GUI) called HyperspecGlu in this study aids the rapid and nondestructive application of HSI data coupled with chemometrics for the determination of glutinous rice quality which includes colour change, golden index, moisture, protein, fat and ash content. The tool simplifies the HSI data processing and glutinous rice quality prediction, featuring data upload, preprocessing, model execution and result visualization through a click-and-run button. Employing three-stage processing techniques which include Savitzky-Golay first derivative techniques for spectral correction, redundant wavelength removal using variable importance space shrinkage approach and predictive model development gave a good prediction accuracy, which makes the HyperspecGlu reliable. Therefore, the HyperspecGlu toolbox is capable of swiftly detecting glutinous rice quality with high accuracy based on the HSI combined with chemometrics and the GUI makes the process available and accessible for users with little or no programming knowledge.</div></div>","PeriodicalId":319,"journal":{"name":"Food Control","volume":"174 ","pages":"Article 111252"},"PeriodicalIF":5.6,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548132","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}
Pub Date : 2025-02-25DOI: 10.1016/j.foodcont.2025.111250
Svetlana Cvetkova , Elke Herrmann , Jutta Keiser , Benedikt Woll , Mario Stahl , Maren Scharfenberger-Schmeer , Elke Richling , Dominik Durner
Ultraviolet (UV) light at 254 nm is well known for its germicidal properties and widely used in food and beverage preservation, though UV light-emitting diodes (UV-LEDs) at 280 nm have advantages over traditional UV mercury vapour lamps and are thus discussed to replace them. This study investigates the efficiency of 280 nm for inactivation of harmful yeast Brettanomyces bruxellensis in white wine as compared to 254 nm. Increasing UV doses were applied in a Riesling wine. Since another treatment wavelength potentially affects reaction kinetics of wine compounds, the chemical and sensory properties were investigated. The microbial analysis revealed greater efficiency of 280 nm compared to 254 nm through comparison by 5-log inactivation dose. It was shown that the Weibull model is suitable to describe the inactivation kinetics of Brettanomyces bruxellensis at 254 and 280 nm. Chemical analyses showed significant differences between UV treatments at 254 and 280 nm for the colour properties and phenol concentrations but not for the investigated volatile compounds. Phenol degradation was more pronounced with increasing UV doses at 280 nm as described by pseudo first-order kinetics. Sensory evaluation of the wine revealed that UV treatment at 280 nm changed odour and taste stronger than at 254 nm. Despite better microbial efficiency, the 280 nm approach seems less suitable for the UV treatment of white wine.
{"title":"Comparing the effect of UV treatment at wavelengths 254 nm and 280 nm: Inactivation of Brettanomyces bruxellensis and impact on chemical and sensory properties of white wine","authors":"Svetlana Cvetkova , Elke Herrmann , Jutta Keiser , Benedikt Woll , Mario Stahl , Maren Scharfenberger-Schmeer , Elke Richling , Dominik Durner","doi":"10.1016/j.foodcont.2025.111250","DOIUrl":"10.1016/j.foodcont.2025.111250","url":null,"abstract":"<div><div>Ultraviolet (UV) light at 254 nm is well known for its germicidal properties and widely used in food and beverage preservation, though UV light-emitting diodes (UV-LEDs) at 280 nm have advantages over traditional UV mercury vapour lamps and are thus discussed to replace them. This study investigates the efficiency of 280 nm for inactivation of harmful yeast <em>Brettanomyces bruxellensis</em> in white wine as compared to 254 nm. Increasing UV doses were applied in a Riesling wine. Since another treatment wavelength potentially affects reaction kinetics of wine compounds, the chemical and sensory properties were investigated. The microbial analysis revealed greater efficiency of 280 nm compared to 254 nm through comparison by 5-log inactivation dose. It was shown that the Weibull model is suitable to describe the inactivation kinetics of <em>Brettanomyces bruxellensis</em> at 254 and 280 nm. Chemical analyses showed significant differences between UV treatments at 254 and 280 nm for the colour properties and phenol concentrations but not for the investigated volatile compounds. Phenol degradation was more pronounced with increasing UV doses at 280 nm as described by pseudo first-order kinetics. Sensory evaluation of the wine revealed that UV treatment at 280 nm changed odour and taste stronger than at 254 nm. Despite better microbial efficiency, the 280 nm approach seems less suitable for the UV treatment of white wine.</div></div>","PeriodicalId":319,"journal":{"name":"Food Control","volume":"174 ","pages":"Article 111250"},"PeriodicalIF":5.6,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548061","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}
Pub Date : 2025-02-25DOI: 10.1016/j.foodcont.2025.111247
Rui Xu , Muhammad Zeeshan Adil , Sidra Jabeen , Khansa , Mahwish Tanveer , Sadia Younis , Bakhtawar Shafique , Long Li
Adulteration of milk and dairy products is considered a deceptive practice for economic gain and poses serious health risks. Conventional techniques, such as chromatography, spectrofluorimetry, immunological methods, and DNA-based procedures, have limitations in terms of complexity, cost, and technical expertise requirements. Non-destructive spectroscopic and imaging techniques have emerged as promising alternatives for the rapid, specific, and sensitive detection of adulterants. Fluorescence spectroscopy, surface-enhanced Raman spectroscopy, near-infrared spectroscopy, mid-infrared spectroscopy, laser-induced breakdown spectroscopy, terahertz spectroscopy, photoacoustic spectroscopy, and nuclear magnetic resonance spectroscopy have all demonstrated their potential for milk analysis and monitoring. Imaging techniques such as hyperspectral imaging, multispectral imaging, Raman imaging, and terahertz imaging provide advantages in terms of sensitivity, specificity, cost-effectiveness, and reliability. However, spectral data include a large number of complex spectral features and peaks, making the direct analysis of spectral data challenging. It is essential to construct quantitative models using chemometrics to identify specific components of dairy products. Chemometrics coupled with these techniques have played a crucial role in expediting adulterant detection and ensuring the traceability and authenticity of dairy products. The additional benefits of employing chemometric models include taxonomic research, counterfeit product detection, process monitoring, geographical origin assessment, and quality control. Exploratory data analysis, classification, discrimination, prediction, and regression methods are commonly used, with multivariate classification models leveraging chemometrics to extract diverse information from spectra for sample classification and differentiation. This review highlights advancements in non-destructive analytical techniques coupled with chemometric modeling for the rapid detection of milk and dairy product adulteration, emphasizing the importance of these methods to ensure food safety and quality, and to facilitate the development of real-time, on-site monitoring platforms.
{"title":"Recent advancements in chemometrics based non-destructive analytical techniques for rapid detection of adulterants in milk and dairy products – A review","authors":"Rui Xu , Muhammad Zeeshan Adil , Sidra Jabeen , Khansa , Mahwish Tanveer , Sadia Younis , Bakhtawar Shafique , Long Li","doi":"10.1016/j.foodcont.2025.111247","DOIUrl":"10.1016/j.foodcont.2025.111247","url":null,"abstract":"<div><div>Adulteration of milk and dairy products is considered a deceptive practice for economic gain and poses serious health risks. Conventional techniques, such as chromatography, spectrofluorimetry, immunological methods, and DNA-based procedures, have limitations in terms of complexity, cost, and technical expertise requirements. Non-destructive spectroscopic and imaging techniques have emerged as promising alternatives for the rapid, specific, and sensitive detection of adulterants. Fluorescence spectroscopy, surface-enhanced Raman spectroscopy, near-infrared spectroscopy, mid-infrared spectroscopy, laser-induced breakdown spectroscopy, terahertz spectroscopy, photoacoustic spectroscopy, and nuclear magnetic resonance spectroscopy have all demonstrated their potential for milk analysis and monitoring. Imaging techniques such as hyperspectral imaging, multispectral imaging, Raman imaging, and terahertz imaging provide advantages in terms of sensitivity, specificity, cost-effectiveness, and reliability. However, spectral data include a large number of complex spectral features and peaks, making the direct analysis of spectral data challenging. It is essential to construct quantitative models using chemometrics to identify specific components of dairy products. Chemometrics coupled with these techniques have played a crucial role in expediting adulterant detection and ensuring the traceability and authenticity of dairy products. The additional benefits of employing chemometric models include taxonomic research, counterfeit product detection, process monitoring, geographical origin assessment, and quality control. Exploratory data analysis, classification, discrimination, prediction, and regression methods are commonly used, with multivariate classification models leveraging chemometrics to extract diverse information from spectra for sample classification and differentiation. This review highlights advancements in non-destructive analytical techniques coupled with chemometric modeling for the rapid detection of milk and dairy product adulteration, emphasizing the importance of these methods to ensure food safety and quality, and to facilitate the development of real-time, on-site monitoring platforms.</div></div>","PeriodicalId":319,"journal":{"name":"Food Control","volume":"174 ","pages":"Article 111247"},"PeriodicalIF":5.6,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519347","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}
Pub Date : 2025-02-24DOI: 10.1016/j.foodcont.2025.111249
Yilin Lin , Xie Xuan , Kabore Manegdebwaoga Arthur Fabrice , Yigang Yu , Lihua Huang , Yehui Zhang
High concentrations of tea polyphenols (TP) negatively affect the color and texture of fish. This study aimed to investigate the effects of alkaline electrolyzed water (AEW) pretreatment combined with low-concentration TP immersion on the physicochemical, biochemical, bacterial structure, and free amino acid composition of grass carp fillets. The results showed that AWE-treated with 5 min (W) and immersion in 0.1 mg/mL TP (T) significantly increased hardness, springiness, and water-holding capacity (WHC), and the initial total viable count (TVC) decreased by 1.11 lg CFU/g and 0.36 lg CFU/g, respectively. Compared with the individual treatments (W or T), the combined treatment of W and T (WT) effectively reduced lipid oxidation and maintained the TVC (5.92 lg CFU/g) and total volatile basic nitrogen (15.56 mg/100 g) within acceptable limits for up to storage for 12 d. The W treatment significantly increased protein solubility and surface hydrophobicity, which is beneficial for promoting interactions between proteins and TP, improving the thermal stability and storage stability of fillets. An increase of Lys, Arg, and Phe concentration in WT treatment could promote a more compact microstructure of MPs. The WT treatment reduced the synergistic effects of Pseudomonas and Acinetobacter and delayed the spoilage of fish fillets. Furthermore, the WT treatment effectively inhibited the abundance of Brochothrix and Serratia, delaying MPs degradation and maintaining the freshness of fillets.
{"title":"Effects of alkaline water-assisted tea polyphenol soaking on the physicochemical, biochemical, bacterial structure, and free amino acid composition of grass carp fillets during storage at 4 °C","authors":"Yilin Lin , Xie Xuan , Kabore Manegdebwaoga Arthur Fabrice , Yigang Yu , Lihua Huang , Yehui Zhang","doi":"10.1016/j.foodcont.2025.111249","DOIUrl":"10.1016/j.foodcont.2025.111249","url":null,"abstract":"<div><div>High concentrations of tea polyphenols (TP) negatively affect the color and texture of fish. This study aimed to investigate the effects of alkaline electrolyzed water (AEW) pretreatment combined with low-concentration TP immersion on the physicochemical, biochemical, bacterial structure, and free amino acid composition of grass carp fillets. The results showed that AWE-treated with 5 min (W) and immersion in 0.1 mg/mL TP (T) significantly increased hardness, springiness, and water-holding capacity (WHC), and the initial total viable count (TVC) decreased by 1.11 lg CFU/g and 0.36 lg CFU/g, respectively. Compared with the individual treatments (W or T), the combined treatment of W and T (WT) effectively reduced lipid oxidation and maintained the TVC (5.92 lg CFU/g) and total volatile basic nitrogen (15.56 mg/100 g) within acceptable limits for up to storage for 12 d. The W treatment significantly increased protein solubility and surface hydrophobicity, which is beneficial for promoting interactions between proteins and TP, improving the thermal stability and storage stability of fillets. An increase of Lys, Arg, and Phe concentration in WT treatment could promote a more compact microstructure of MPs. The WT treatment reduced the synergistic effects of <em>Pseudomonas</em> and <em>Acinetobacter</em> and delayed the spoilage of fish fillets. Furthermore, the WT treatment effectively inhibited the abundance of <em>Brochothrix</em> and <em>Serratia</em>, delaying MPs degradation and maintaining the freshness of fillets.</div></div>","PeriodicalId":319,"journal":{"name":"Food Control","volume":"174 ","pages":"Article 111249"},"PeriodicalIF":5.6,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527155","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}
In suspect screening or nontargeted analysis via LC high-resolution MS (LC-HRMS), high-accuracy identification typically relies on retention times (RTs) and MS–MS spectra. However, RTs are difficult to obtain due to the scarcity of reference standards. Here, we developed a Quantitative Structure Retention Relationships (QSRR) -based RT prediction model using machine learning, specifically for plant toxins implicated in accidental food poisoning. A dataset for QSRR model development was generated using the molecular descriptors (MDs) and experimental RTs of 524 compounds. QSRR models were constructed as regression models derived from the relationship between experimental RTs and MDs using 10 machine learning algorithms. The QSRR model with support vector regression (SVR) outperformed the other QSRR models in generalization on the analyzed dataset (R2: 0.972, mean absolute error: 183 [approximately 1.6 min], mean absolute percentage error [MAPE]: 6%; Q2: 0.875, MAE: 584 [approximately 2.0 min], MAPE: 15%). Furthermore, the SVR QSRR model successfully predicted the RTs of nine plant toxins with errors of ±0.5 min. Thus, this model enhances the confidence level of plant toxin identification via LC-HRMS.
{"title":"Optimal machine learning algorithm for prediction model for retention times of plant toxins","authors":"Masaru Taniguchi , Shoichiro Noguchi , Hidenobu Kawashima , Jun Sugiura , Tomoyuki Tsuchiyama , Tomiaki Minatani , Hitoshi Miyazaki , Kei Zaitsu","doi":"10.1016/j.foodcont.2025.111251","DOIUrl":"10.1016/j.foodcont.2025.111251","url":null,"abstract":"<div><div>In suspect screening or nontargeted analysis via LC high-resolution MS (LC-HRMS), high-accuracy identification typically relies on retention times (RTs) and MS–MS spectra. However, RTs are difficult to obtain due to the scarcity of reference standards. Here, we developed a Quantitative Structure Retention Relationships (QSRR) -based RT prediction model using machine learning, specifically for plant toxins implicated in accidental food poisoning. A dataset for QSRR model development was generated using the molecular descriptors (MDs) and experimental RTs of 524 compounds. QSRR models were constructed as regression models derived from the relationship between experimental RTs and MDs using 10 machine learning algorithms. The QSRR model with support vector regression (SVR) outperformed the other QSRR models in generalization on the analyzed dataset (<em>R</em><sup>2</sup>: 0.972, mean absolute error: 183 [approximately 1.6 min], mean absolute percentage error [MAPE]: 6%; <em>Q</em><sup>2</sup>: 0.875, MAE: 584 [approximately 2.0 min], MAPE: 15%). Furthermore, the SVR QSRR model successfully predicted the RTs of nine plant toxins with errors of ±0.5 min. Thus, this model enhances the confidence level of plant toxin identification via LC-HRMS.</div></div>","PeriodicalId":319,"journal":{"name":"Food Control","volume":"174 ","pages":"Article 111251"},"PeriodicalIF":5.6,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511319","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}
Pub Date : 2025-02-24DOI: 10.1016/j.foodcont.2025.111248
Cameron A. Bardsley , Kaicie S. Chasteen , Samantha H. Sherman , Vera Arthur , Ajit K. Mahapatra , Brendan A. Niemira , David I. Shapiro-Ilan
In-shell pecans are generally conditioned in water prior to shelling which presents the potential for foodborne pathogen cross-contamination. The use of sanitizers in conditioning water can be an effective way to reduce cross-contamination. The objectives of this study were to determine the effectiveness of varying concentrations of peracetic acid (PAA) in conditioning water at reducing Salmonella loads and preventing cross-contamination on in-shell pecans compared to sodium hypochlorite (NaOCl). In-shell pecans were inoculated with two concentrations (high and low) of a rifampicin resistant five-strain Salmonella cocktail. Inoculated pecans and uninoculated pecans were placed in water treatments (PAA: 0, 30, and 90 ppm; NaOCl: 1000 ppm free chlorine) and were sampled at 0, 2, 15, 60, and 240 min. There were significant (P < 0.05) Salmonella reductions at each timepoint on pecans conditioned in water containing 30 and 90 ppm PAA with the final reduction being 3.4 ± 0.9 and 4.2 ± 0.5 log CFU/g, respectively, after 60 min. The 90 ppm PAA treatment was significantly more effective at preventing Salmonella cross-contamination compared to the 0 and 30 ppm PAA treatments. Up to 4.0 log CFU/g transfer of Salmonella was observed from inoculated to uninoculated pecans in the 0 ppm treatment. Both 90 ppm PAA and 1000 ppm NaOCl treatments were found effective at reducing Salmonella levels on pecans (4.2 ± 1.2 and 4.0 ± 1.4 log CFU/g, respectively) and preventing cross-contamination over a 4-h conditioning period. The PAA was an effective sanitizer compared to NaOCl at preventing cross-contamination and also reducing Salmonella populations on in-shell pecans during washing and conditioning.
{"title":"Peracetic acid washes reduce Salmonella load on the surface of in-shell pecans and prevents cross-contamination between pecans during conditioning","authors":"Cameron A. Bardsley , Kaicie S. Chasteen , Samantha H. Sherman , Vera Arthur , Ajit K. Mahapatra , Brendan A. Niemira , David I. Shapiro-Ilan","doi":"10.1016/j.foodcont.2025.111248","DOIUrl":"10.1016/j.foodcont.2025.111248","url":null,"abstract":"<div><div>In-shell pecans are generally conditioned in water prior to shelling which presents the potential for foodborne pathogen cross-contamination. The use of sanitizers in conditioning water can be an effective way to reduce cross-contamination. The objectives of this study were to determine the effectiveness of varying concentrations of peracetic acid (PAA) in conditioning water at reducing <em>Salmonella</em> loads and preventing cross-contamination on in-shell pecans compared to sodium hypochlorite (NaOCl). In-shell pecans were inoculated with two concentrations (high and low) of a rifampicin resistant five-strain <em>Salmonella</em> cocktail. Inoculated pecans and uninoculated pecans were placed in water treatments (PAA: 0, 30, and 90 ppm; NaOCl: 1000 ppm free chlorine) and were sampled at 0, 2, 15, 60, and 240 min. There were significant (P < 0.05) <em>Salmonella</em> reductions at each timepoint on pecans conditioned in water containing 30 and 90 ppm PAA with the final reduction being 3.4 ± 0.9 and 4.2 ± 0.5 log CFU/g, respectively, after 60 min. The 90 ppm PAA treatment was significantly more effective at preventing <em>Salmonella</em> cross-contamination compared to the 0 and 30 ppm PAA treatments. Up to 4.0 log CFU/g transfer of <em>Salmonella</em> was observed from inoculated to uninoculated pecans in the 0 ppm treatment. Both 90 ppm PAA and 1000 ppm NaOCl treatments were found effective at reducing <em>Salmonella</em> levels on pecans (4.2 ± 1.2 and 4.0 ± 1.4 log CFU/g, respectively) and preventing cross-contamination over a 4-h conditioning period. The PAA was an effective sanitizer compared to NaOCl at preventing cross-contamination and also reducing <em>Salmonella</em> populations on in-shell pecans during washing and conditioning.</div></div>","PeriodicalId":319,"journal":{"name":"Food Control","volume":"175 ","pages":"Article 111248"},"PeriodicalIF":5.6,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579809","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}
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