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- 摘要
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- 实验方案
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A new formaldehyde optical sensor: detecting milk adulteration
摘要： A sensor consisting of an optic fibre with the exposed tip coated with a the polyoxometalate salt [(C4H9)4N]4H[PMo10V2O40], specially designed to be insoluble in water, which UV-Vis spectrum changed in contact with formaldehyde, is presented. The sensor limit of detection for formaldehyde was 0.2 mg L-1, and the limit of quantification was 0.6 mg L-1, which were close to the conventional spectrophotometric method values of 0.2 mg L-1 and 0.5 mg L-1, respectively, and lower than the tolerable limit for ingested food. The sensor was tested for formaldehyde quantification in milk, as its deliberate addition is a matter of concern. The results obtained analysing formaldehyde in milk samples by the optical sensor and by the conventional method were not statistically different (α=0.05).
关键词： acetylacetone method,milk,formaldehyde,polyoxometalate,food adulteration,optical fibre sensor
更新于2025-09-23 15:21:01
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Rapid analysis of food raw materials adulteration using laser direct infrared spectroscopy and imaging
摘要： The objective of this study was to assess the application of the Laser Direct Infrared (LDIR) imaging system as a rapid screening technology for detection, identification, and semi-quantitation of adulterants in food ingredients. Forty-five samples of skimmed milk powder, thirty-one samples of soy protein isolate, thirty-five samples of chicken meat powder, thirty-two samples of pea protein isolate and six samples of wheat flour were dry blended adulterated with nitrogen-rich compounds and bulking agents at concentrations of 1.0 to 15.0% (w/w). In addition, ten samples of skimmed milk powder were wet blended with food adulterants at 5.0% and 10.0% (w/w) to check the LDIR performance when different fraudulent processes are applied. The results from this study shows that LDIR can be used as a rapid untargeted screening method that are independent of adulterants to detect, identify and semi-quantify food adulterants in dry blended samples. In most samples, the technology accurately identified all nitrogen-rich compounds and bulking agents present in the dry blended samples. In addition, the technology shows sensitivity of 82% for samples adulterated at 1% and sensitivity from 92% to 100% for samples adulterated at ≥ 5% economic adulteration. On the other hand, the detection and identification of food adulterants in samples prepared by wet blending process was more challenging than dry blended samples because mid-infrared technology may not be sensitive enough to detect adulterants if they are dissolved or if hidden within the particles.
关键词： Food adulteration,mid-infrared,laser direct infrared imaging,vibrational spectroscopy,untargeted method,raw materials
更新于2025-09-23 15:19:57
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Tea types classification with data fusion of UV–Vis, synchronous fluorescence and NIR spectroscopies and chemometric analysis
摘要： The potential of selected spectroscopic methods - UV-Vis, synchronous fluorescence and NIR as well a data fusion of the measurements by these methods - for the classification of tea samples with respect to the production process was examined. Four classification methods - Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Regularized Discriminant Analysis (RDA) and Support Vector Machine (SVM) - were used to analyze spectroscopic data. PCA analysis was applied prior to classification methods to reduce multidimensionality of the data. Classification error rates were used to evaluate the performance of these methods in the classification of tea samples. The results indicate that black, green, white, yellow, dark, and oolong teas, which are produced by different methods, are characterized by different UV-Vis, fluorescence, and NIR spectra. The lowest error rates in the calibration and validation data sets for individual spectroscopies and data fusion models were obtained with the use of the QDA and SVM methods, and did not exceed 3.3% and 0.0%, respectively. The lowest classification error rates in the validation data sets for individual spectroscopies were obtained with the use of RDA (12,8%), SVM (6,7%), and QDA (2,7%), for the UV-Vis, SF, and NIR spectroscopies, respectively. NIR spectroscopy combined with QDA outperformed other individual spectroscopic methods. Very low classification errors in the validation data sets - below 3% - were obtained for all the data fusion data sets (SF+UV-Vis, SF+NIR, NIR+UV-Vis combined with the SVM method). The results show that UV-Vis, fluorescence and near infrared spectroscopies may complement each other, giving lower errors for the classification of tea types.
关键词： Fluorescence spectroscopy,Food adulteration,NIR,Teas classification,Multivariate data analysis,Data fusion,UV-Vis
更新于2025-09-10 09:29:36