Non-destructive quantitative analysis of carotene content in carrots using Raman spectroscopy

Bridging artificial intelligence and fucoxanthin for the recovery and quantification from microalgae

Fucoxanthin is a carotenoid that possesses various beneficial medicinal properties for human well-being. However, the current extraction technologies and quantification techniques are still lacking in terms of cost validation, high energy consumption, long extraction time, and low yield production. To date, artificial intelligence (AI) models can assist and improvise the bottleneck of fucoxanthin extraction and quantification process by establishing new technologies and processes which involve big data, digitalization, and automation for efficiency fucoxanthin production. This review highlights the application of AI models such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS), capable of learning patterns and relationships from large datasets, capturing non-linearity, and predicting optimal conditions that significantly impact the fucoxanthin extraction yield. On top of that, combining metaheuristic algorithm such as genetic algorithm (GA) can further improve the parameter space and discovery of optimal conditions of ANN and ANFIS models, which results in high R2 accuracy ranging from 98.28% to 99.60% after optimization. Besides, AI models such as support vector machine (SVM), convolutional neural networks (CNNs), and ANN have been leveraged for the quantification of fucoxanthin, either computer vision based on color space of images or regression analysis based on statistical data. The findings are reliable when modeling for the concentration of pigments with high R2 accuracy ranging from 66.0% - 99.2%. This review paper has reviewed the feasibility and potential of AI for the extraction and quantification purposes, which can reduce the cost, accelerate the fucoxanthin yields, and development of fucoxanthin-based products.

Detection of moisture ratio and carotenoid compounds in mamey (Pouteria sapota) fruit during dehydration process using spectroscopic techniques

This work presents the study of the moisture ratio and carotenoid compounds in dried mamey (Pouteria sapota) using non-invasive spectroscopic techniques. The drying behavior of mamey at 64 °C by a homemade solar dryer is analyzed by fitting the experimental data to four different mathematical drying models. In addition, this result is compared with other drying techniques, namely by heat chamber with natural convection at temperatures of 50 °C and 60 °C. The results show that the Lewis model is the one that best fits the experimental moisture ratio curve of mamey. On the other hand, Near-Infrared and Terahertz spectroscopic techniques are used to estimate the moisture ratio, since water absorption is most sensitive at these frequencies. Fourier Transform Infrared-attenuated total reflectance and Raman spectroscopy are performed to detect the carotenoid compounds in dried mamey. This compound has important applications in the food industry and health benefits. To our knowledge, there are few studies on the dehydration of Pouteria sapota as well as its characterization using spectroscopic techniques for the detection of moisture ratio and carotenoid content; therefore, this study can be useful in agriculture and food sectors when detailed information about the cited parameters is needed.

Human developmental toxicity mechanism of polybrominated biphenyl exposure and health risk regulation strategy for special populations

Polybrominated biphenyls (PBBs) can bioaccumulate in nature and are toxic to humans. Long-time exposure to PBBs in pregnant women can lead to the birth of an infant with abnormal conditions. Hence, in this study, we used molecular docking, molecular dynamics, Taguchi experimental design, and fractional factorial experimental design to identify the developmental toxicity characteristics of 10 typical developmental toxic pollutants such as PBBs to which humans are frequently exposed. Furthermore, the correlation and sensitivity analyses of molecular developmental toxicity and structural parameters were performed. The molecular key structural parameters of the pollutants affecting human development were screened. Moreover, the supplementary food factors that could alleviate the developmental toxicity of pollutants were screened to develop supplementary food schemes to prevent or alleviate human developmental toxicity in the special population (e.g., pregnant women, infants) exposed to the pollutants. The results showed that the developmental toxicity was controlled by the main effects of the 10 pollutants. Among the 10 pollutants with developmental toxicity, the most significant pollutant with the main effects was PBB-153 (37.06%). In addition, the correlation and sensitivity analyses of the molecular developmental toxicity of the pollutants and structural parameters showed that the total energy value and infrared C-H vibration frequency of the pollutants were significantly correlated with human developmental toxicity. Accordingly, 15 supplementary food cofactors were selected for the Taguchi experiment design, among which the top seven cofactors were designed by fractional factorial analysis. The most significant cofactor that alleviated the developmental toxicity of PBB-153 exposure was the combination of carotene and docosahexaenoic acid (DHA), with an improvement of 17.28%. The combination of carotene and DHA significantly alleviated the effects of toxicity caused by most of the other pollutants, indicating that the selected supplementary food has certain universality. In this study, we developed a method to identify the characteristics of the developmental toxicity of pollutant exposure and developmental toxicity alleviation. Our study provided theoretical support for the regulation strategy of developmental toxicity caused by pollutants such as PBBs.