Food Analytical Methods最新文献

{"title":"VisDist-Net: A New Lightweight Model for Fruit Freshness Classification","authors":"Semih Demirel,&nbsp;Oktay Yıldız","doi":"10.1007/s12161-024-02716-4","DOIUrl":"10.1007/s12161-024-02716-4","url":null,"abstract":"<div><p>Agricultural production is of vital importance for humanity and the agricultural economy. Enhancing food security in agriculture can increase agricultural production and also help alleviate food scarcity. Also, the early detection of plant diseases can be crucial for quality agricultural products. The use of embedded software in Internet of Things devices for quality control processes has become quite widespread. These software applications require lightweight models. Therefore, a new model named the vision distillation network (VisDist-Net) has been developed to address real-world problems in agricultural production. This model aims to increase agricultural productivity by classifying three different fruits as rotten and fresh. An open-source dataset was used for this classification. VisDist-Net is a model created based on knowledge distillation. In the VisDist-Net model, knowledge is distilled from a vision transformer to a hybrid convolutional neural network (cnn). The strengths of both models have been combined by creating a hybrid student convolutional neural network through the fusion of feature vectors from resnet18 and mobilenetv1 models. This distillation process enables the creation of a high-performance lightweight model suitable for real-world applications. The VisDist-Net model has yielded quite promising results in this endeavor, achieving an f1-score of 0.9945 and an area under the curve (AUC) score of 0.9967.</p></div>","PeriodicalId":561,"journal":{"name":"Food Analytical Methods","volume":"18 2","pages":"229 - 244"},"PeriodicalIF":2.6,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of Antibiotics in Migratory Goat Milk Using QuEChERS-HPLC Approach and Human Health Risk Assessment in Himalayan Region, India","authors":"Abhishek Sharma,&nbsp;Atul Kumar","doi":"10.1007/s12161-024-02713-7","DOIUrl":"10.1007/s12161-024-02713-7","url":null,"abstract":"<div><p>Antibiotic residue in milk poses significant risks to consumers’ health and economies. The current investigation aimed to analyze the occurrence of tetracycline residues in raw milk obtained from goats reared under a migratory system by nomadic pastoralists of Western Himalayan region, India, using the QuEChERS approach. The method was found to be accurate (recoveries, 87.07 to 97.70%), precise (RSD &lt; 10%), and sensitive (CCα, 1.61–9.40 ng/mL) as per European Commission guidelines. Quantitative analyses of 223 milk samples by validated high-performance liquid chromatography revealed that 18 samples (8.07%) had oxytetracycline and tetracycline residues in the concentration range of ND–307.5 ng/mL and 08 samples (3.6%) exceeded the maximum residual limits (MRLs) of 100 ng/mL for tetracycline in milk as set by the European Union and Codex Alimentarius Commission. The health risk assessments based on estimated daily consumer intake revealed that the hazard index for detected antibiotics is &lt; 1, indicating negligible acute risks at current contamination levels. However, the detection of antibiotics even in trace levels in migratory goat milk is a matter of solicitude and therefore requiring attention. This study highlights the importance of antimicrobial surveillance through green chemistry–based approaches like “QuEChERS” and awareness programs for shepherds to protect and promote human, animal, and environmental health.</p></div>","PeriodicalId":561,"journal":{"name":"Food Analytical Methods","volume":"18 2","pages":"218 - 228"},"PeriodicalIF":2.6,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of Goat Milk Adulterated with Cow Milk Based on Total Synchronous Fluorescence Spectroscopy Combined with CNN","authors":"Xiaoyan Wang,&nbsp;Tao Wang,&nbsp;Rendong Ji,&nbsp;Huichang Chen,&nbsp;Hailin Qin,&nbsp;Zihan Huang","doi":"10.1007/s12161-024-02714-6","DOIUrl":"10.1007/s12161-024-02714-6","url":null,"abstract":"<div><p>Goat milk is rich in short-chain fatty acids, which are beneficial to health; however, the adulteration of goat milk with cow milk in the market poses a significant risk to individuals allergic to cow milk. This study utilizes the differences in total synchronous fluorescence spectra (TSFS) between cow milk and goat milk, combined with convolutional neural network (CNN), to detect goat milk adulteration. An improved algorithm was introduced: a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) incorporating a convolutional attention mechanism. Data filtering was performed using a combination of Mahalanobis distance and K-means algorithm. Four classical CNN classifiers—AlexNet, DenseNet121, VGG16, and ResNet50—were evaluated under consistent training conditions. Comparative analysis shows that using a 1:2 sample enhancement ratio in conjunction with AlexNet plus WGAN-GP is most effective, achieving an accuracy of 97.78% after hyperparameter optimization. This study demonstrates that integrating TSFS with CNN offers a robust method for milk fingerprint recognition.</p></div>","PeriodicalId":561,"journal":{"name":"Food Analytical Methods","volume":"18 2","pages":"202 - 217"},"PeriodicalIF":2.6,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of IoT Enabled Deep Learning Model for Indian Food Classification: An Approach Based on Differential Evaluation","authors":"Mohit Agarwal,&nbsp;Amit Kumar Dwivedi,&nbsp;Dibyanarayan Hazra,&nbsp;Suneet Kumar Gupta,&nbsp;Deepak Garg","doi":"10.1007/s12161-024-02701-x","DOIUrl":"10.1007/s12161-024-02701-x","url":null,"abstract":"<div><p>Due to its extensive use in several areas, deep learning has attracted much interest in the past 10 years. Furthermore, decision-making applications for IoT devices are required, and the number of such devices is growing exponentially. Conversely, IoT devices are subject to resource constraints such as limited power, memory, and computation power. As a result, deep learning models that require less storage space and have a shorter inference time are more popular than traditional models. In the proposed article, we have discussed a differential evaluation-based approach for optimizing the storage space with a significant decrease in inference time without compromising the accuracy too much. We used an openly available Indian food dataset for the experimental work, using popular pre-trained architectures for classification purposes. We then compress the trained models using the differential evaluation approach. The simulation results show that the VGG16 architecture is compressed by 46.15%, with a decrease in precision of 1.91%.</p></div>","PeriodicalId":561,"journal":{"name":"Food Analytical Methods","volume":"18 2","pages":"172 - 189"},"PeriodicalIF":2.6,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative Detection of Zn2+ in Infant Formula and Living Cells Using Quinoline-Based Fluorescent Probes","authors":"Wen Lu,&nbsp;Jichao Chen,&nbsp;Jiongya Tang,&nbsp;Yutian chen,&nbsp;Yingying Ma,&nbsp;Wuhan Sang,&nbsp;Sicheng Feng,&nbsp;Shilong Yang,&nbsp;Yanqin Wang,&nbsp;Xu Li","doi":"10.1007/s12161-024-02709-3","DOIUrl":"10.1007/s12161-024-02709-3","url":null,"abstract":"<div><p>Zinc is an essential trace element and its deficiency has been related to skin conditions, Alzheimer’s disease, and some types of cancer. Therefore, detecting zinc ions in the human body with high sensitivity is important. Here, two “turn-on” quinoline-based fluorescent probes (<i>E</i>)-2-((2-(quinolin-2-yl) hydrazono) methyl) phenol (<b>QSP-H</b>) and (<i>E</i>)-4-chloro-2-((2-(quinolin-2-yl) hydrazono) methyl) phenol (<b>QSP-Cl</b>) were fabricated for the detection of Zn²⁺. Both the <b>QSP-H</b> and <b>QSP-Cl</b> revealed low LOD (71 nM for <b>QSP-H</b>, 67 nM for <b>QSP-Cl</b>) and high selectivity, and worked across a broad pH range (3 ‒12 for <b>QSP-H</b>, 3 ‒11 for <b>QSP-Cl</b>). The HRMS, ¹H NMR titration, DFT calculations and Job’s plot analysis were employed to explore the mechanism of Zn²⁺ detection through <b>QSP-H</b> and <b>QSP-Cl</b>. <b>QSP-H</b> and <b>QSP-Cl</b> were effectively applied to the quantitative assessment of Zn²⁺ in two infant formula samples and to the bioimaging-based detection of exogenous Zn²⁺ in living cells.</p></div>","PeriodicalId":561,"journal":{"name":"Food Analytical Methods","volume":"18 2","pages":"190 - 201"},"PeriodicalIF":2.6,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multivariate Optimization of a Green Microwave-Assisted Hydrogen Peroxide Digestion of Vegetable Oils and Subsequent Elemental Determination Via Inductively Coupled Plasma–Optical Emission Spectrometry","authors":"Basil K. Munjanja,&nbsp;Oluseyi S. Salami,&nbsp;Kedibone N. Mashale,&nbsp;Philiswa N. Nomngongo,&nbsp;Nomvano Mketo","doi":"10.1007/s12161-024-02708-4","DOIUrl":"10.1007/s12161-024-02708-4","url":null,"abstract":"<div><p>A microwave-assisted digestion technique based on dilute hydrogen peroxide (MW-AHPD) was developed for multielemental determination in vegetable oils. The determination of ten trace elements (As, Cd, Cr, Cu, Fe, Pb, Ni, Sn, V, and Zn) was conducted via inductively coupled plasma optical emission spectroscopy (ICP-OES) after digestion. The most influential parameters were investigated by using multivariate optimization tools (two-level full factorial and central composite design) with percent recovery as the chemometric response. The optimum conditions were 2.0 mol L⁻¹ (H₂O₂]), 156 °C (digestion temperature),  0.1 g (sample mass) and 50 min (digestion time). Under the optimized conditions, the efficiency of digestion was evaluated based on the residual carbon content (RCC) of the final digests. The RCC values were very low, ranging from 0.84 to 1.60% (m/m). The greenness of the technique was evaluated using the Analytical Eco-scale, and the proposed method was considered an excellent green analysis method with a final score of 90. The accuracy of the optimized MW-AHPD was evaluated by spiking sunflower, olive, and peanut oils at concentrations of 2.5 and 5.0 μg L⁻¹, and excellent recoveries between 90.3 and 107.3% were reported. The accuracy of the MW-AHPD method was compared with that of microwave-assisted digestion using concentrated HNO₃, and there was no significant difference between the two methods. The limits of detection ranged from 0.026 to 14.6 μg L⁻¹. On the other hand, the interday and intraday precisions were less than 6.67 and 6.96%, respectively. The method was successfully applied to determine the concentrations of trace elements in 5 vegetable oils on the South African market. Thus, MW-AHPD-ICP–OES is applicable for the determination of trace elements in vegetable oils.</p></div>","PeriodicalId":561,"journal":{"name":"Food Analytical Methods","volume":"18 2","pages":"161 - 171"},"PeriodicalIF":2.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12161-024-02708-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of Carmine in Black Tea Based on UV–Vis Absorption Spectroscopy and Machine Learning","authors":"Xiaoyan Wang,&nbsp;Huichang Chen,&nbsp;Rendong Ji,&nbsp;Hailin Qin,&nbsp;Qinxin Xu,&nbsp;Tao Wang,&nbsp;Ying He,&nbsp;Zihan Huang","doi":"10.1007/s12161-024-02705-7","DOIUrl":"10.1007/s12161-024-02705-7","url":null,"abstract":"<div><p>Carmine is a common synthetic pigment widely used in food processing, pharmaceutical dyeing, and other fields. Black tea is a popular beverage among many people, and its tea pigments have antioxidant, antiviral, anti-inflammatory, and antibacterial effects. However, excessive addition of carmine in black tea can pose a threat to human health. This article applies ultraviolet–visible (UV–vis) absorption spectroscopy technology to detect the carmine component in black tea and constructs a prediction model for the carmine content in black tea based on the Levenberg–Marquardt back propagation (LMBP) neural network and random forest (RF) algorithm. Firstly, 75 different concentrations of black tea-carmine solutions were prepared, and UV–vis absorption spectra were measured. Then, different methods were used to preprocess the spectra in different wavelength ranges, resulting in the optimal characteristic wavelength range of 400–600 nm, with the best preprocessing method being the combination of SG smoothing and normalization. Finally, the LMBP neural network and RF methods were applied to construct content prediction models for the carmine in black tea. The coefficient of determination (<i>R</i>²) of the LMBP model corresponding to the test set was 0.99996, with the root mean square error (RMSE) of 1.0257 × 10⁻⁵, while the <i>R</i>² of the RF model based on the full spectral wavelength was 0.98339, with the RMSE of 1.1686 × 10⁻⁴. The <i>R</i>² value using the traditional Lambert–Beer law of the test set is 0.96673, while the <i>R</i>² value based on the nonlinear fitting method is 0.98074. This article verifies the superiority of the LMBP method in predicting the content of carmine in black tea through experiments, providing important reference value for tea quality supervision.</p></div>","PeriodicalId":561,"journal":{"name":"Food Analytical Methods","volume":"18 2","pages":"149 - 160"},"PeriodicalIF":2.6,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantification of Trihalomethanes in Nile Tilapia Fillets Sanitized with Chlorine","authors":"Nelson de Abreu Delvaux Júnior,&nbsp;Maria Eliana Lopes Ribeiro de Queiroz,&nbsp;André Fernando de Oliveira,&nbsp;Antônio Augusto Neves,&nbsp;Fernanda Fernandes Heleno,&nbsp;Amanda Maria Leal Pimenta,&nbsp;Flávio Pinto Monção,&nbsp;Samy Pimenta,&nbsp;Louiza Lourrane Mendes Pereira,&nbsp;Luís Felipe Rodrigues Costa,&nbsp;Luciano Pereira Rodrigues","doi":"10.1007/s12161-024-02706-6","DOIUrl":"10.1007/s12161-024-02706-6","url":null,"abstract":"<div><p>Fish is a food with high nutritional value, but its quickly perishable nature requires a hygiene process to maintain product quality and increase shelf life. The aim of this study was to evaluate different concentrations of chlorine in the water used to sanitize Nile tilapia fillets on the formation of trihalomethanes (THM) in fresh and frozen products at different storage times. A completely randomized experimental design was used, in a 2 × 3 split-plot arrangement, with 15 replications; the plots consisted of fresh and frozen fish fillets, and the subplots consisted of the three chlorine concentrations in the water for sanitizing the fillets (100, 175, and 250 mg L⁻¹). Fresh fish analysis showed the following variations in THM levels: 1.4 to 6.8 μg kg⁻¹ (chloroform), 0.5 to 3.2 μg kg⁻¹ (bromodichloromethane), and 0.4 to 1.2 μg kg⁻¹ (dibromochloromethane). Bromoform ranged from 3.7 μg kg⁻¹ to undetected levels in the different tested storage times. Frozen fish analyses showed concentrations ranging from 2.1 to 5.2 μg kg⁻¹ (chloroform) and 0.6 to 2.0 μg kg⁻¹ (bromodichloromethane), while dibromochloromethane and bromoform levels were below the technique’s quantification limits in all storage times. THM formation depends on the binomial chlorine concentration in the washing water and time storage; fresh fish presented higher THM levels than frozen fish.</p></div>","PeriodicalId":561,"journal":{"name":"Food Analytical Methods","volume":"18 1","pages":"140 - 147"},"PeriodicalIF":2.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143373174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Green and Low-Cost Method for Selenium Analysis in Beans by Photochemical Vapor Generation Coupled to Atomic Absorption Spectrometry (PVG-AAS)","authors":"Ana Clara C. Aragão,&nbsp;Jane Kelly S. Brito,&nbsp;Felipe S. Ferreira,&nbsp;Gisele S. Lopes","doi":"10.1007/s12161-024-02703-9","DOIUrl":"10.1007/s12161-024-02703-9","url":null,"abstract":"<div><p>Selenium is an essential element and the main source of it is food. The ingestion of food rich in this element as nuts (Brazil nuts) is important to keep the body healthy. In this work, the development of a green and low-cost methodology for selenium analysis in beans was carried out using the photochemical vapor generation method coupled to atomic absorption spectrometry. The efficiency of the photochemical vapor generation (PVG) was optimized by evaluating the experimental conditions, such as type and concentration of the organic acid, flow rate of the carrier gas, and sample UV irradiation time. The formation of volatile species was more efficient using 6% v v⁻¹ acetic acid, carrier gas flow rate of 10 mL min⁻¹, and 20-s UV irradiation time. The method of standard addition calibration curves was performed to analyze total Se in green bean samples acquired in the market of different cities of northeast and north regions of Brazil. The accuracy of the proposed method was tested comparing results for Se content with hydride generation (HG) in a sample of Ceara state. The analyzed samples showed total Se content varying from 0.9 to 2.2 mg kg⁻¹ in dry matter of green beans. The amount of Se found in green beans is within the expected range when compared to other types of beans reported in the literature.</p></div>","PeriodicalId":561,"journal":{"name":"Food Analytical Methods","volume":"18 1","pages":"121 - 128"},"PeriodicalIF":2.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143373404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Untargeted Metabolomics Based on UHPLC-HRMS for Authentication of Chicken Meat Supplied by Different Slaughter Methods of Halal, Non-Halal, and Shubha","authors":"Vevi Maritha,&nbsp;Puri Ratna Kartini,&nbsp;Nur Ihda Farikhatin Nisa,&nbsp;Alice Rivera,&nbsp;Firman Rezaldi,&nbsp;Rudi Heryanto,&nbsp;Avip Kurniawan,&nbsp;Mohammad Yuwono","doi":"10.1007/s12161-024-02699-2","DOIUrl":"10.1007/s12161-024-02699-2","url":null,"abstract":"<div><p>To protect consumers from non-halal and shubha-halal foods, it is essential to authenticate chicken meat based on its slaughtering process. The objective of the present study is to authenticate the halalness of chicken meat based on the slaughter process. Untargeted metabolomics, utilizing UHPLC-HRMS combined with chemometrics, offers a selective and accurate method for verifying the halal status of chicken meat based on the slaughter process. The results of this research identified 28 metabolite profiles, with creatine, carnosine, and 3-methylhistidine being the most prominent metabolites. Principal component analysis (PCA) clearly distinguished the metabolite profiles of chicken meat slaughtered using different methods. Additionally, cluster analysis effectively grouped chicken meat based on similarities in metabolite profiles. The correlation network revealed that 21 types of metabolites are interrelated in the halal authentication process. Partial least squares discriminant analysis (PLS-DA) accurately identified 13 potential biomarkers for halal authentication, including creatine, betaine, 2-amino-1,3,4-octadecanetriol, L-isoleucine, L-phenylalanine, L-histidine, L-glutamic acid, L-glutathione, DL-glutamine, taurine, carnosine, and acetyl-L-carnitine. Overall, untargeted metabolomics combined with UHPLC-HRMS and chemometrics represents a promising method for authenticating the halal status of chicken meat, distinguishing between halal, non-halal, shubha-halal, and mixtures of halal with non-halal or shubha-halal meat.</p></div>","PeriodicalId":561,"journal":{"name":"Food Analytical Methods","volume":"18 1","pages":"129 - 139"},"PeriodicalIF":2.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143373403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}