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The spectroscopic discrimination of chili pepper samples according to geographical origin was executed using analytical techniques coupled with machine learning. First, laser ablation-inductively coupled plasma mass spectrometry (LA-ICP-MS), X-ray fluorescence (XRF), and near-infrared (NIR) spectroscopy were chosen for simple and rapid sample measurements. Second, to secure discrimination accuracy, eXtreme Gradient Boosting (XGBoost), a tree-based ensemble technique, was adopted as a potential classifier. Also, for explainable machine learning modeling, SHaply Additive exPlanation (SHAP) values of employed variables were calculated to assess how they contribute to the discrimination. The use of XGBoost improved discrimination accuracies in all three measurements compared to k-nearest neighbor (k-NN), support vector machine (SVM), and partial least squares-discriminant analysis (PLS-DA). The accuracy was 96.2 % using the LA-ICP-MS data. When the XRF and NIR data were combined, the accuracy improved to 97.5 %. The accuracy improvement was attributed to the combination of complementary atomic and molecular spectroscopic signatures of the samples.

Abstract Background Red pepper paste is a common ingredient used in food in Korea. The discrimination of the geographical origin of agricultural products is important to protect the agricultural industry and customers from the misinformation regarding the product origin. Several studies have attempted to identify the geographical origin of red pepper based on its characteristic features using diverse methods, such as inorganic elemental analysis. However, similar studies on red pepper pastes have not been conducted thus far. Results In, this study, we established methods based on inductively coupled plasma optical emission spectrometry (ICP-AES) and inductively coupled plasma mass spectrometry (ICP-MS) for determining inorganic elements in red pepper pastes. The limit of detection (LOD) of ICP-AES was in the range of 0.006–0.531 mg∙kg−1 and the limit of quantification (LOQ) was 0.017–1.593 mg∙kg−1. In addition, LOD and LOQ ranges for ICP-MS were 0.001–1.553, and 0.002–5.176 μg∙kg−1, respectively. The concentrations of Ca, K, Mg, Na, P, S, Cr, Mn, Co, Ni, Cu, Ga, As, Sr, Zr, Mo, Pd, Cd, Sn, Sb, Ce, Pt, Pb, and U were high in the Korean red pepper paste. All the employed discrimination models could clearly distinguish between Korean and Chinese red pepper pastes. In particular, among the four different models, CDA showed the most accurate ability to discriminate the geological origin of Korean and Chinese red pepper paste compared to that achieved using the other models with 100% accuracy. Conclusions Based on, the findings of this study, the use of ICP-AES and ICP-MS analyses for discriminating the inorganic elements in food products in combination with the aforementioned statistical analysis models could help the mitigation of issues associated with the misinformation of the geographical origin of agricultural products, aiding customer protection. Graphical Abstract

Abstract Background White standard Dendranthema grandiflorum (D. grandiflorum) is widely used for ornamental purposes. The volatile organic components (VOCs) of D. grandiflorum vary depending on the geographical origin and cultivar, which influence the market value. Here, we applied headspace extraction coupled with gas chromatography–mass spectrometry (HS–GC–MS) for the first time to determine the different origins (Korea, China, Vietnam) of D. grandiflorum in three genotypes (Baekgang, Jinba, Iwa-no-hakusen) based on volatile fingerprints and to assess the correlation among the metabolites identified in Korean and non-Korean D. grandiflorum. Results A total of 41 VOCs, mainly monoterpenoids, sesquiterpenoids, alcohols, and fatty acids, were identified. Principal component analysis showed that instead of geographical origin, genotype influences D. grandiflorum fingerprints. Cultivar discrimination was mainly affected by the metabolites associated with the alpha-terpinyl cation pathway. The orthogonal partial least squares discriminant analysis model achieved 100% and 93.3% accuracies in the calibration and validation sets, respectively. The results of volcano plots and clustering analysis, sesquiterpenoids were significantly more abundant in non-Korean than in Korean, whereas monoterpenoids were abundant in Korean samples. Conclusions 41 makers confirmed robustness maintenance over two years. These findings can be useful for reliably identifying the geographical origin of D. grandiflorum and providing a comprehensive understanding of VOCs in D. grandiflorum cultivars grown in different countries of origins. Graphical Abstract

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.

A simultaneous analysis method using LC-MS/MS and GC-MS/MS was developed for improving the analysis accuracy of various pesticide residues. Samples spiked with 247 and 34 pesticide residues were analyzed by LC-MS/MS and GC-MS/MS, respectively. The method was verified by measuring sensitivity, linearity, selectivity, precision, and accuracy. LC-MS/MS LOD and LOQ values were determined to be 0.03–0.5 μg/kg, and 0.6–1.5 μg/kg, respectively, while the corresponding values for GC-MS/MS were found to be 0.9–2.0 μg/kg and from 3.0–5.7 μg/kg, respectively. Most of the 34 common residues had recovery rates of 70–120%. The results showed that the developed method can be a reliable and more accurate than the general multi component analysis method.

This study examined the volatile organic compounds (VOCs) and metabolites of glucosinolate (GLS) contained in kimchi and analyzed GLS using myrosinase. The analysis was conducted using gas chromatography-time of flight (GC-TOF), and VOC and the metabolite quantities were detected and analyzed. Based on 22 samples, tests were conducted, and 12 metabolites and 52 VOCs were found. When the detected metabolites were compared in general, the rate of isothiocyanate, which is well known for its anticancer effects and various other activities, was the highest. A total of 52 VOCs, including 15 aliphatic hydrocarbons, 7 acids, and 6 alcohols, were detected by GC-TOF. Therefore, the analytical methods provide a good basis to examine VOC and GLS metabolites; furthermore, the methods are of great help to secure excellent kimchi and evaluate its quality.