Attenuated Total Reflectance-Fourier transform infrared spectroscopy coupled with chemometrics for the rapid detection of coconut water adulteration

Assessment of the authenticity of coconut water (Cocos nucifera L.) samples using digital images and chemometric techniques

This study proposes using digital colorimetry combined with unsupervised pattern recognition techniques to obtain a molecular fingerprint profile that allows detection and identification of the non-destructive authenticity of coconut water samples. It also intends to classify the samples sold as in nature, adulterated, or industrialized. The samples were purchased at street markets and local stores in the state of Bahia, northeastern Brazil. The digital images were obtained through direct analysis without pre-treatment of the samples. Then, the combination values of color histograms in RGB channels were extracted using Chemostat software. Principal component analysis and hierarchical clustering contributed to the classification of the samples. It was possible to prove that digital colorimetry is a useful tool that allows confirming the authenticity of foods quickly and at a low cost. It can contribute to the inspection by regulatory agencies, in addition to following the principles of green analytical chemistry.

Application of a microfluidic electronic tongue based on impedance spectroscopy for coconut water analysis

The food industry has grown with the demands for new products and their authentication, which has not been accompanied by the area of analysis and quality control, thus requiring novel process analytical technologies for food processes. An electronic tongue (e-tongue) is a multisensor system that can characterize complex liquids in a fast and simple way. Here, we tested the efficacy of an impedimetric microfluidic e-tongue setup - comprised by four interdigitated electrodes (IDE) on a printed circuit board (PCB), with four pairs of digits each, being one bare sensor and three coated with different ultrathin nanostructured films with different electrical properties - in the analysis of fresh and industrialized coconut water. Principal Component Analysis (PCA) was applied to observe sample differences, and Partial Least Squares Regression (PLSR) was used to predict sample physicochemical parameters. Linear Discriminant Analysis (LDA) and Partial Least Square - Discriminant Analysis (PLS-DA) were compared to classify samples based on data from the e-tongue device. Results indicate the potential application of the microfluidic e-tongue in the identification of coconut water composition and determination of physicochemical attributes, allowing for classification of samples according to soluble solid content (SSC) and total titratable acidity (TTA) with over 90% accuracy. It was also demonstrated that the microfluidic setup has potential application in the food industry for quality assessment of complex liquid samples.

Physicochemical and Nutritional Properties of Vegetable Oils from Brazil Diversity and Their Applications in the Food Industry

In this study, the oils of açaí, passion fruit, pequi, and guava were submitted to physicochemical analysis to investigate their potential application in the food industry. Gas chromatography associated with mass spectroscopy showed that oleic and linoleic acids are mainly responsible for the nutritional quality of açaí, passion fruit, pequi, and guava oils, which exhibited 46.71%, 38.11%, 43.78%, and 35.69% of the former fatty acid, and 18.93%, 47.64%, 20.90%, and 44.72% of the latter, respectively. The atherogenicity index of the oils varied from 0.11 to 0.65, while the thrombogenicity index was 0.93 for açaí, 0.35 for guava, and 0.3 for passion fruit oils, but 1.39 for pequi oil, suggesting that the use of the first three oils may lead to a low incidence of coronary heart disease. Thermogravimetry showed that all tested oils were thermally stable above 180 °C; therefore, they can be considered resistant to cooking and frying temperatures. In general, the results of this study highlight possible applications of these oils in the food industry, either in natura or in typical food production processes.

3D Printing Technology: Role in Safeguarding Food Security

The rising threats to food security include several factors, such as population growth, low agricultural investment, and poor distribution systems. Consequently, food insecurity results from a confluence of issues, including diseases, processing limitations, and distribution deficiencies. Food insecurity usually occurs in vulnerable areas where certain technologies and traditional food safety testing are not a viable solution for foodborne disease detection. In this regard, 3D printing technologies and 3D printed sensors open the platform to produce portable, accurate, and low-cost sensors that address the gaps and challenges in food security. In this paper, we discuss the perspective role of 3D printed sensors in food security in terms of food safety and food quality monitoring to provide reliable access to nutritious, affordable food. In each section, we highlight the advantages of 3D printing technology in terms of cost-effectiveness, accuracy, accessibility, and reproducibility compared to traditional manufacturing methodologies. Recent developments in robotic technologies for mechanization, such as food handling with soft grippers, are also discussed. Lastly, we delve into the applications of advanced 3D printing technologies in agricultural monitoring, particularly the future of plant wearables, environmental sensing, and overall plant health monitoring.

Multiple fingerprint and pattern recognition analysis on polysaccharides of four edible mushrooms

Quality analysis of edible mushrooms based on polysaccharides is generally difficult due to their complicated structures and hard separation. Here, multiple fingerprint analysis of polysaccharides based on chromatographic and spectrometric techniques were developed, and then applied in comparative analysis of Auricularia heimuer (AH), Auricularia cornea (AC), Auricularia cornea 'Yu Muer' (ACY) and Tremella fuciformis (TF). Firstly, polysaccharides were obtained with the molecular weights between 1.783 × 106 and 6.774 × 106 Da. Then, complete hydrolysis by TFA and enzyme digestion by cellulase were employed and subsequently analyzed by HPLC-UV, GC-MS, HILIC-HPLC-ELSD and HILIC-HPLC-ESI--HCD-MS/MS, and ATR-FT-IR were used to characterize the functional groups of intact polysaccharides. By chemometric analysis, differential markers of d-xyl, l-fuc, l-arb, d-glc, disaccharide and hexasaccharide were selected, and AC and ACY were proved to be same species from the viewpoint of polysaccharides firstly. Furthermore, the structures of oligomers with DPs of 2-8 and →4)-β-d-Glcp-(1→ unit with different contents were inferred by combinatory analysis of ESI--MS/MS, glycosidic linkage, monosaccharide compositions and functional groups. In conclusion, the combinatory method of multiple fingerprint and pattern recognition is powerful not only for structural elucidation of polysaccharides, but also for quality analysis and species differentiation of edible mushrooms from the perspective of biological polysaccharides.

Extraction and Encapsulation of Phytocompounds of Poniol Fruit via Co-Crystallization: Physicochemical Properties and Characterization

Poniol (Flacourtia jangomas) has beneficial health effects due to its high polyphenolic and good antioxidant activity content. This study aimed to encapsulate the Poniol fruit ethanolic extract to the sucrose matrix using the co-crystallization process and analyze the physicochemical properties of the co-crystalized product. The physicochemical property characterization of the sucrose co-crystallized with the Poniol extract (CC-PE) and the recrystallized sucrose (RC) samples was carried out through analyzing the total phenolic content (TPC), antioxidant activity, loading capacity, entrapment yield, bulk and traped densities, hygroscopicity, solubilization time, flowability, DSC, XRD, FTIR, and SEM. The result revealed that the CC-PE product had a good entrapment yield (76.38%) and could retain the TPC (29.25 mg GAE/100 g) and antioxidant properties (65.10%) even after the co-crystallization process. Compared to the RC sample, the results also showed that the CC-PE had relatively higher flowability and bulk density, lower hygroscopicity, and solubilization time, which are desirable properties for a powder product. The SEM analysis showed that the CC-PE sample has cavities or pores in the sucrose cubic crystals, which proposed that the entrapment was better. The XRD, DSC, and FTIR analyses also showed no changes in the sucrose crystal structure, thermal properties, and functional group bonding structure, respectively. From the results, we can conclude that co-crystallization increased sucrose's functional properties, and the co-crystallized product can be used as a carrier for phytochemical compounds. The CC-PE product with improved properties can also be utilized to develop nutraceuticals, functional foods, and pharmaceuticals.

Integration of Non-targeted Proteomics Mass Spectrometry with Machine Learning for Screening Cooked Beef Adulterated Samples

The degradation of ingredients in heat-processed meat products makes their authentication challenging. In this study, protein profiles of raw beef, chicken, duck, pork, and binary simulated adulterated beef samples (chicken-beef, duck-beef, and pork-beef) and their heat-processed samples were obtained by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Heat-stable characteristic proteins were found by screening the overlapping characteristic protein ion peaks of the raw and corresponding heat-processed samples, which were discovered by partial least-squares discriminant analysis. Based on the 36 heat-stable characteristic proteins, qualitative classification for the raw and heat-processed meats was achieved by extreme gradient boosting. Moreover, quantitative analysis via partial least squares regression was applied to determine the adulteration ratio of the simulated adulterated beef samples. The validity of the approach was confirmed by a blind test with the mean accuracy of 97.4%. The limit of detection and limit of quantification of this method were determined to be 5 and 8%, respectively, showing its practical aspect for the beef authentication.

Identification and quantification of adulterated honey by Raman spectroscopy combined with convolutional neural network and chemometrics

In this study, Raman spectroscopy combined with convolutional neural network (CNN) and chemometrics was used to achieve the identification and quantification of honey samples adulterated with high fructose corn syrup, rice syrup, maltose syrup and blended syrup, respectively. The shallow CNNs utilized to analyze honey mixed with single-variety syrup classified samples into four categories by the adulteration concentration with more than 97% accuracy, and the general CNN model for simultaneously detecting honey adulterated with any type of syrup obtained an accuracy of 94.79%. The established CNNs had the best performance compared with several chemometric classification algorithms. In addition, partial least square regression (PLS) successfully predicted the purity of honey mixed with single syrup, while coefficients of determination and root mean square errors of prediction were greater than 0.98 and less than 3.50, respectively. Therefore, the proposed methods based on Raman spectra have important practical significance for food safety and quality control of honey products.