Multivariate Statistical Analysis for the Classification of Sausages Based on Physicochemical Attributes, Using Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS).

Sausage is a convenient food that is widely consumed in the world and in Vietnam. Due to the rapid development of this product, the authenticity of many famous brands has faded by the rise of adulteration. Therefore, in this study, principal component analysis (PCA) was combined with chemical analysis to identify 6 sausage brands. Sausage samples were dried and then ground to a fine powder for both instrumental analyses of attenuated total reflectance-Fourier transform infrared (ATR-FTIR) and inductively coupled plasma-mass spectrometry (ICP-MS). Dried measurements of ATR-FTIR was performed directly on the ZnSe crystal, while elemental data were obtained through microwave digestion before the ICP-MS analysis. Principal component analysis (PCA) within the framework software of XLSTAT and STATISTICA 12 was performed on the spectroscopy and elemental dataset of sausage samples. PCA visualized the distinction of 6 sausage brands on both datasets of ATR-FTIR and ICP-MS. The classification on the spectroscopy profile showed that although more than 90% variation of the dataset was explained on the first two PCs, the difference between several brands was not detected as the distribution of data overlapped with one another. The PCA observation of the elemental composition on PC1 and PC3 has separated the sausage brands into 6 distinctive groups. Besides, several key elements contributed to the brands' identification have been detected, and the most distinctive elements are Na, K, Ca, and Ba. PCA visualization showed the feasibility of the classification of sausage samples from different brands when combined with the results of FT-IR and ICP-MS methods. The experiment was able to differentiate the sausages from the 5 brands using multivariate statistics.

Classification of Vietnamese Cashew Nut (Anacardium occidentale L.) Products Using Statistical Algorithms Based on ICP/MS Data: A Study of Food Categorization.

Fingerprinting techniques, which utilize the unique chemical and physical properties of food samples, have emerged as a promising approach for food authentication and traceability. Recent studies have demonstrated significant advancements in food authentication through the use of fingerprinting methods, such as multivariate statistical analysis techniques applied to trace elements and isotope ratios. However, further research is required to optimize these methods and ensure their validity and reliability in real-world applications. In this study, the inductively coupled plasma mass spectrometry (ICP-MS) analytical method was employed to determine the content of 21 elements in 300 cashew nut (Anacardium occidentale L.) samples from 5 brands. Multivariate statistical methods, such as principal components analysis (PCA), were employed to analyze the data obtained and establish the provenance of the cashew nuts. While cashew nuts are widely marketed in many countries, no universal method has been utilized to differentiate the origin of these nuts. Our study represents the initial step in identifying the geographical origin of commercial cashew nuts marketed in Vietnam. The analysis showed significant differences in the means of 21 of the 40 analyzed elements among the cashew nut samples from the 5 brands, including 7Li, 11B, 24Mg, 27Al, 44Ca, 48Ti, 51V, 52Cr, 55Mn, 57Fe, 60Ni, 63Cu, 66Zn, 93Nb, 98Mo, 111Cd, 115In, 121Sb, 138Ba, 208Pb, and 209Bi. The PCA analysis indicated that the cashew nut samples can be accurately classified according to their original locations. This research serves as a prerequisite for future studies involving the combination of elemental composition analysis with statistical classification methods for the accurate establishment of cashew nut provenance, which involves the identification of key markers for the original discrimination of cashew nuts.

Towards a Standardized Approach for the Geographical Traceability of Plant Foods Using Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and Principal Component Analysis (PCA).

This paper presents a systematic literature review focused on the use of inductively coupled plasma mass spectrometry (ICP-MS) combined with PCA, a multivariate technique, for determining the geographical origin of plant foods. Recent studies selected and applied the ICP-MS analytical method and PCA in plant food geographical traceability. The collected results from many previous studies indicate that ICP-MS with PCA is a useful tool and is widely used for authenticating and certifying the geographic origin of plant food. The review encourages scientists and managers to discuss the possibility of introducing an international standard for plant food traceability using ICP-MS combined with PCA. The use of a standard method will reduce the time and cost of analysis and improve the efficiency of trade and circulation of goods. Furthermore, the main steps needed to establish the standard for this traceability method are reported, including the development of guidelines and quality control measures, which play a pivotal role in providing authentic product information through each stage of production, processing, and distribution for consumers and authority agencies. This might be the basis for establishing the standards for examination and controlling the quality of foods in the markets, ensuring safety for consumers.

Predicting the severity of COVID-19 patients using the CD24-CSF1R index in whole blood samples.

Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, has become one of the most serious public health crises worldwide. Most infected people are asymptomatic but are still able to spread the virus. People with mild or moderate illnesses are likely to recover without hospitalization, while critically ill patients face a higher risk of organ injury or even death. In this study, we aimed to identify a novel biomarker that can predict the severity of COVID-19 patients. Clinical information and RNA-seq data of leukocytes from whole blood samples with and without a COVID-19 diagnosis (n = 100 and 26, respectively) were retrieved from the National Center for Biotechnology Information Gene Expression Omnibus database. Raw data were processed using the Transcripts Per Million (TPM) method and then transformed using log2 (TPM+1) for normalization. The CD24-CSF1R index was established. Violin plots, Kaplan-Meier curves, ROC curves, and multivariate Cox proportional hazards regression analyses were performed to evaluate the prognostic value of the established index. The CD24-CSF1R index was significantly associated with ICU admission (n = 50 ICU, 50 non-ICU) and ventilatory status (n = 42 ventilation, 58 non-ventilation) with p = 4.186e-11 and p = 1.278e-07, respectively. The ROC curve produced a relatively accurate prediction of ICU admission with an AUC of 0.8524. Additionally, patients with a high index had significantly fewer mechanical ventilation-free days than patients with a low index (p = 6.07e-07). Furthermore, the established index showed a strong prognostic ability for the risk of using a ventilator in the multivariate Cox regression model (p < 0.001). The CD24-CSF1R index was significantly associated with COVID-19 severity. The established index could have potential implications for prognosis, disease severity stratification, and clinical management.

Rapid Identification of Geographical Origin of Commercial Soybean Marketed in Vietnam by ICP-MS.

Inductively coupled plasma mass spectrometry (ICP-MS) analytical method was used to determine the content of 40 elements in 38 soybean samples (Glycine Max) from 4 countries. Multivariate statistical methods, such as principal components analysis (PCA), were performed to analyze the obtained data to establish the provenance of the soybeans. Although soybean is widely marketed in many countries, no universal method is used to discriminate the origin of these cereals. Our study introduced the initial step to the identification of the geographical origin of commercial soybean marketed in Vietnam. The analysis pointed out that there are significant differences in the mean of 33 of the 40 analyzed elements among 4 countries' soybean samples, namely, 11B, 27Al, 44Ca, 45Sc, 47Ti, 55Mn, 56Fe, 59Co, 60Ni, 63Cu, 66Zn, 69Ga, 75As, 78Se, 85Rb, 88Sr, 89Y, 90Zr, 93Nb, 95Mo, 103Rh, 137Ba, 163Dy, 165Ho, 175Lu, 178Hf, 181Ta, 182W, 185Re, 197Au, 202Hg, 205Tl, and 208Pb. The PCA analysis showed that the soybean samples can be classified correctly according to their original locations. This research can be used as a prerequisite for future studies of using the combination of elemental composition analysis with statistical classification methods for an accurate provenance establishment of soybean, which determined a variation of key markers for the original discrimination of soybean.

Multielement Analysis of Pakchoi (Brassica rapa L. ssp. chinensis) by ICP-MS and Their Classification according to Different Small Geographical Origins.

Statistical interpretation of the concentrations of 42 elements, determined using solution-based inductively coupled plasma mass spectrometry (ICP-MS) analysis and multivariate statistical methods, such as principal components analysis (PCA), was used to establish the provenance of pakchoi (Brassica rapa L. ssp. chinensis) from 6 areas in Ha Noi, Vietnam. Although pakchoi is widely cultivated and manufactured, no universal method is used to discriminate the origin of this vegetable. Our study introduced for the first time a method to classify pakchoi in small geographical areas. 42 metallic elements of pakchoi were detected by ICP-MS, which were further analyzed using multivariate statistical analysis to perform clusters based on the geographical locations. Eleven elements, i.e., 28Si; 56Fe; 59Co; 63Cu; 69Ga; 75As; 85Rb; 93Nb; 107Ag; 118Sn, and 137Ba, were identified as discriminators to distinguish pakchoi from those areas. Results from this study presented a new method to discriminant the geographical origins of pakchoi, which could apply to other types of vegetables on the food market.