Online quantitative substrate, product, and cell concentration in citric acid fermentation using near-infrared spectroscopy combined with chemometrics

Integration of electronic nose, electronic tongue, and colorimeter in combination with chemometrics for monitoring the fermentation process of Tremella fuciformis

This study proposes an efficient method for monitoring the submerged fermentation process of Tremella fuciformis (T. fuciformis) by integrating electronic nose (e-nose), electronic tongue (e-tongue), and colorimeter sensors using a data fusion strategy. Chemometrics was employed to establish qualitative identification and quantitative prediction models. The Pearson correlation analysis was applied to extract features from the e-nose and tongue sensor arrays. The optimal sensor arrays for monitoring the submerged fermentation process of T. fuciformis were obtained, and four different data fusion methods were developed by incorporating the colorimeter data features. To achieve qualitative identification, the physicochemical data and principal component analysis (PCA) results were utilized to determine three stages of the fermentation process. The fusion signal based on full features proved to be the optimal data fusion method, exhibiting the highest accuracy across different models. Notably, random forest (RF) was shown to be the most accurate pattern recognition method in this paper. For quantitative prediction, partial least squares regression (PLSR) and support vector regression (SVR) were employed to predict the sugar content and dry cell weight during fermentation. The best respective predictive R2 values for reducing sugar, tremella polysaccharide and dry cell weight were found to be 0.965, 0.988, and 0.970. Furthermore, due to its ability to capture nonlinear data relationships, SVR had superior performance in prediction modeling than PLSR. The results demonstrated that the combination of electronic sensor fusion signals and chemometrics provided a promising method for effectively monitoring T. fuciformis fermentation.

Comparison Study of the Physicochemical Properties, Amino Acids, and Volatile Metabolites of Guangdong Hakka Huangjiu

The physicochemical properties, amino acids, and volatile metabolites of 20 types of Guangdong Hakka Huangjiu were systematically compared in this study. Lower sugar contents were detected in LPSH, ZJHL-1, and GDSY-1, but the total sugar contents of the other types of Guangdong Hakka Huangjiu were more than 100 g/L (which belonged to the sweet type). Among them, a lower alcohol content was found in GDSY-1 (8.36 %vol). There was a significant difference in the organic acid and amino acid composition among the 20 Guangdong Hakka Huangjiu samples, especially the amino acid composition. However, bitter amino acids as the major amino acids accounted for more than 50% of the total amino acids. A substantial variation in volatile profiles was also observed among all types of Guangzhou Hakka Huangjiu. Interestingly, MZSK-1 had different volatile profiles from other Guangzhou Hakka Huangjiu samples. According to gas chromatography olfactometry (GC-O), most of the aroma-active ingredients identified in Guangdong Hakka Huangjiu were endowed with a pleasant aroma of "fruity".

rAAV Manufacturing: The Challenges of Soft Sensing during Upstream Processing

Recombinant adeno-associated virus (rAAV) is the most effective viral vector technology for directly translating the genomic revolution into medicinal therapies. However, the manufacturing of rAAV viral vectors remains challenging in the upstream processing with low rAAV yield in large-scale production and high cost, limiting the generalization of rAAV-based treatments. This situation can be improved by real-time monitoring of critical process parameters (CPP) that affect critical quality attributes (CQA). To achieve this aim, soft sensing combined with predictive modeling is an important strategy that can be used for optimizing the upstream process of rAAV production by monitoring critical process variables in real time. However, the development of soft sensors for rAAV production as a fast and low-cost monitoring approach is not an easy task. This review article describes four challenges and critically discusses the possible solutions that can enable the application of soft sensors for rAAV production monitoring. The challenges from a data scientist's perspective are (i) a predictor variable (soft-sensor inputs) set without AAV viral titer, (ii) multi-step forecasting, (iii) multiple process phases, and (iv) soft-sensor development composed of the mechanistic model.