JOURNAL OF FOOD COMPOSITION AND ANALYSIS
·
2025
·
IF 4.6
Linlin Wu,Qilin Xu,Cheng Su,Xianggang Yin,Xinyi Huo,Xiaohan Zhao,Yifeng Zhou,Jun Huang
Anji white tea, a premium green tea renowned for its distinctive characteristics and considerable economic value, traditionally relies on sensory evaluation and chemical analysis for quality assessment. Sensory methods, though intuitive, are subjective and time consuming, while chemical techniques such as gas chromatography and high performance liquid chromatography, provide only a limited view of a few chemical components. This study introduces hyperspectral imaging (HSI) technology to evaluate Anji white tea quality by simultaneously capturing spectral and image data. Spectral data were preprocessed and analyzed to extract features related to catechins, tea polyphenols, and free amino acids. Image data texture features were extracted using the gray-level co-occurrence matrix, and significant features related to tea grades were identified through correlation analysis. Classification models using support vector machine and K-nearest neighbors (KNN) algorithms were developed. To enhance model accuracy, two data fusion strategies—low-level and mid-level—were applied. The results showed that both fusion strategies outperformed single-data models, with the KNN model using mid-level fusion achieving 100 % accuracy in both the training and test sets. This study demonstrates the potential of HSI and data fusion in improving the accuracy of tea quality assessment, providing a solid framework for the scientific evaluation of Anji white tea.
研究领域:
食品科学
·
农业工程
·
计算机科学
✓ 已验证
产品关联
DOI
高影响因子
ADVANCED MATERIALS
·
2023
·Vol. 35
·期 19
·2210637
·
IF 29.4
Yingying Zhou,Xianfeng Ping,Yusi Guo,Boon Chin Heng,Yijun Wang,Yanze Meng,Shengjie Jiang,Yan Wei,Binbin Lai,Xuehui Zhang,Xuliang Deng
Current functional assessment of biomaterial-induced stem cell lineage fate in vitro mainly relies on biomarker-dependent methods with limited accuracy and efficiency. Here a “Mesenchymal stem cell Differentiation Prediction (MeD-P)” framework for biomaterial-induced cell lineage fate prediction is reported. MeD-P contains a cell-type-specific gene expression profile as a reference by integrating public RNA-seq data related to tri-lineage differentiation (osteogenesis, chondrogenesis, and adipogenesis) of human mesenchymal stem cells (hMSCs) and a predictive model for classifying hMSCs differentiation lineages using the k-nearest neighbors (kNN) strategy. It is shown that MeD-P exhibits an overall accuracy of 90.63% on testing datasets, which is significantly higher than the model constructed based on canonical marker genes (80.21%). Moreover, evaluations of multiple biomaterials show that MeD-P provides accurate prediction of lineage fate on different types of biomaterials as early as the first week of hMSCs culture. In summary, it is demonstrated that MeD-P is an efficient and accurate strategy for stem cell lineage fate prediction and preliminary biomaterial functional evaluation.
研究领域:
干细胞生物学
·
生物医学工程
·
机器学习
·
人工智能
·
生物材料科学
✓ 已验证
产品关联
DOI
PubMed
高影响因子
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
·
2025
·
IF 4.6
Zhenli Sun,Yiyan Zhang,Zijin Hong,Jingjing Du
Surface-enhanced Raman spectroscopy (SERS) has emerged as a powerful analytical tool for dye monitoring in environmental waters; however, challenges remain in resolving complex mixtures due to spectral overlap and limited signal reproducibility. To address this issue, a dual-channel SERS system integrated with machine learning (ML) was developed. Positively and negatively charged polyelectrolyte-modified Fe 3 O 4 @SiO 2 -Au substrates were employed for the selective detection of anionic and cationic dyes, respectively. Benefiting from the magnetic properties of the substrates, the system enables simultaneous and efficient capture of charge-contrasting dye mixtures. Several classification ML models, including linear discriminant analysis (LDA), k-nearest neighbor (KNN), and decision tree, were constructed to distinguish SERS data from various mixed dyes. The best-performing models, LDA and KNN, achieved classification accuracies exceeding 92 %, and a multimodel ensemble algorithm combining LDA and KNN was designed to harness the strengths of both models, resulting in a classification accuracy of 98 % for mixed dyes. This integrated approach enhances the efficiency and precision of Raman spectroscopic analysis while reducing the need for manual intervention, offering strong potential for high-precision pollutant detection and real-world application in analytical chemistry.
研究领域:
分析化学
·
光谱学
·
环境监测
·
机器学习
·
纳米技术
✓ 已验证
产品关联
DOI
PubMed
高影响因子
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
·
2023
·
IF 1.5
Mingju Chen,Anle Cui,Zhengxu Duan,Xingzhong Xiong
Currently, evaluating the quality of strong-flavor Baijiu (SFB) heavily relies on subjective sensory analysis, resulting in large deviations in evaluation. However, as there are no existing evaluation criteria for SFB quality, this study aimed to extract trace components and design an evaluation model using gas chromatography–mass spectrometry (GC–MS). First, the key component data was analyzed using principal component analysis (PCA) and sparse principal component analysis (SPCA) to identify the most important principal components that represent the SFB samples. Second, KNN, DT, SVM, and BP analyses were then employed on the principal component data to determine the grade of the SFB samples. Finally, a price prediction model based on SPCA+BP was established to objectively evaluate the quality and price of SFB. The experimental results show that the proposed method can effectively realize the distinction and price prediction of SFB.
研究领域:
食品科学
·
分析化学
·
机器学习
·
化学计量学
✓ 已验证
DOI
高影响因子
Food Science of Animal Resources
·
2021
·
IF 2.471
Xiao-Guang Dong,Li-Bing Gao,Hai-Jun Zhang,Jing Wang,Kai Qiu,Guang-Hai Qi,Shu-Geng Wu
This study discriminated fatty acid profile and flavor characteristics of BeijingYou Chicken (BYC) as a precious local breed and Dwarf Beijing You Chicken (DBYC)eggs. Fatty acid profile and flavor characteristics were analyzed to identifydifferences between BYC and DBYC eggs. Four classification algorithms were usedto build classification models. Arachidic acid, oleic acid (OA), eicosatrienoicacid, docosapentaenoic acid (DPA), hexadecenoic acid, monounsaturated fattyacids (MUFA), polyunsaturated fatty acids (PUFA), unsaturated fatty acids (UFA)and 35 volatile compounds had significant differences in fatty acids andvolatile compounds by gas chromatography-mass spectrometry (GC-MS)(p<0.05). For fatty acid data, k-nearest neighbor (KNN) and supportvector machine (SVM) got 91.7% classification accuracy. SPME-GC-MS datafailed in classification models. For electronic nose data, classificationaccuracy of KNN, linear discriminant analysis (LDA), SVM and decision tree wasall 100%. The overall results indicated that BYC and DBYC eggs could bediscriminated based on electronic nose with suitable classification algorithms.This research compared the differentiation of the fatty acid profile andvolatile compounds of various egg yolks. The results could be applied toevaluate egg nutrition and distinguish avian eggs.
研究领域:
食品科学
·
化学计量学
·
动物科学
·
分析化学
✓ 已验证
产品关联
DOI
PubMed
高影响因子
MICROCHIMICA ACTA
·
2023
·Vol. 190
·期 12
·1-13
·
IF 5.7
Zhu Yaodi,Tian Jiaqi,Li Miaoyun,Zhao Lijun,Shi Jiyong,Liu Weijia,Liu Shijie,Liang Dong,Zhao Gaiming,Xu Lina,Yang Shufeng
A new surface-enhanced Raman spectroscopy (SERS) biosensor of Graphene@Ag-MLF composite structure has been fabricated by loading AgNPs on graphene films. The response of the biosensor is based on plasmonic sensing. The results showed that the enhancement factor of three different spores reached 10 7 based on the Graphene@Ag-MLF substrate. In addition, the SERS performance was stable, with good reproducibility (RSD<3%). Multivariate statistical analysis and chemometrics were used to distinguish different spores. The accumulated variance contribution rate was up to 96.35% for the top three PCs, while HCA results revealed that the spectra were differentiated completely. Based on optimal principal components, chemometrics of KNN and LS-SVM were applied to construct a model for rapid qualitative identification of different spores, of which the prediction set and training set of LS-SVM achieved 100%. Finally, based on the Graphene@Ag-MLF substrate, the LOD of three different spores was lower than 10 2 CFU/mL. Hence, this novel Graphene@Ag-MLF SERS substrate sensor was rapid, sensitive, and stable in detecting spores, providing strong technical support for the application of SERS technology in food safety. Graphical abstract
研究领域:
分析化学
·
纳米技术
·
生物传感器
·
食品安全
·
光谱学
·
化学计量学
✓ 已验证
DOI
高影响因子
FOOD CHEMISTRY
·
2024
·
IF 8.5
Yolandani,Dandan Liu,Fredy Agil Raynaldo,Mokhtar Dabbour,Xueli Zhang,Zhongyuan Chen,Qingzhi Ding,Lin Luo,Haile Ma
The bitterness of soy protein isolate hydrolysates prepared using five proteases at varying degree of hydrolysis (DH) and its relation to physicochemical properties, i.e., surface hydrophobicity (H 0 ), relative hydrophobicity (RH), and molecular weight (MW), were studied and developed for predictive modelling using machine learning. Bitter scores were collected from sensory analysis and assigned as the target, while physicochemical properties were assigned as features. The modelling involved data pre-processing with local outlier factor; model development with support vector machine, linear regression, adaptive boosting and K-nearest neighbors algorithms; and performance evaluation by 10-fold stratified cross-validation. The results indicated that alcalase hydrolysates were the most bitter, followed by protamex, flavorzyme, papain, and bromelain. Distinctive correlation results were found among the physicochemical properties, influenced by the disparity of each protease. Among the features, the combination of RH-MW fitted various classification models and resulted in the best prediction performance.
研究领域:
食品科学
·
化学工程
·
机器学习
·
感官评价
✓ 已验证
产品关联
DOI
PubMed
高影响因子
危险品化学品经营许可证（带存储）