Use of LC-Orbitrap MS and FT-NIRS with multivariate analysis to determine geographic origin of Boston butt pork

Determination of the geographical origin of chicken (breast and drumstick) using ICP-OES and ICP-MS: Chemometric analysis

This study aimed to develop a geographical origin discrimination analytical method for chicken breasts and drumsticks based on inductively coupled plasma (ICP). The sixty elements were set as variables, and the geographical origin discrimination analysis was conducted through chemometrics. In orthogonal partial least square discriminant analysis (OPLS-DA), twenty-three variable importance in projection (VIP) elements were selected in chicken breasts, and twenty-eight VIP elements were selected in drumsticks. The importance of the selected elements was displayed by the area under the curve (AUC) value of the receiver operating characteristic (ROC). Verification of OPLS-DA was performed through permutation test and good results were obtained. A heatmap was also used as a method for determining the geographical origin, and each top element discriminant classification was 100 % accurate, as determined through canonical discriminant analysis (CDA). This method shows potential as a food analysis tool and can accurately determine the geographic origin of chicken.

Geographical discrimination of dried chili peppers using femtosecond laser ablation-inductively coupled plasma-mass spectrometry (fsLA-ICP-MS)

This study presents a method for discriminating the geographical origin of dried chili peppers using femtosecond laser ablation-inductively coupled plasma-mass spectrometry (fsLA-ICP-MS) and multivariate analysis, such as orthogonal partial least squares discriminant analysis (OPLS-DA), heatmap analysis, and canonical discriminant analysis (CDA). Herein, 102 samples were analyzed for the content of 33 elements using optimized conditions of 200 Hz (repetition rate), 50 μm (spot size), and 90% (energy). Significant differences in count per second (cps) values of the elements were observed between domestic and imported peppers, with variations of up to 5.66 times (¹³³Cs). The OPLS-DA model accuracy achieved an R² of 0.811 and a Q² of 0.733 for distinguishing dried chili peppers of different geographical origins. The variable importance in projection (VIP) and s-plot identified elements 10 and 3 as key to the OPLS-DA model, and in the heatmap, six elements were estimated to be significant in discriminating between domestic and imported samples. Furthermore, CDA showed a high accuracy of 99.02%. This method can ensure food safety for consumers, and accurately determine the geographic origin of agricultural products.