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LCZ696 is a novel treatment for patients suffering from heart failure that combines the two active pharmaceutical ingredients sacubitril and valsartan in a single chemical compound. While valsartan is an established drug substance, a new manufacturing process suitable for large-scale commercial production had to be developed for sacubitril. The use of chemocatalysis, biocatalysis, and flow chemistry as state-of-the-art technologies allowed to efficiently build up the structure of sacubitril and achieve the defined performance targets.

The objective of this study was to establish quantitative evaluation models for fish microbiological quality analysis based on electronic tongue technique coupled with nonlinear pattern recognition algorithms. Crucian carp stored at 4C were used. A commercial electronic tongue system was employed. The total viable counts (TVCs) of fish samples were measured by the classical microbiological plating method. Partial least square regression, support vector regression (SVR) and back propagation neural network (BP‐NN) were applied comparatively to predict TVC values. The multivariate regression models were evaluated by the root mean square error of prediction (RMSEP) and the correlation coefficient in prediction set (Rpre). Results revealed that the performance of BP‐NN model was superior to that of PLS model and SVR model. The RMSEP and Rpre of the BP‐NN model for TVC prediction were 0.211 ln colony‐forming unit (cfu)/g and 0.993, respectively. This study showed that electronic tongue together with BP‐NN model could be a reliable technique for the detection of fish microbiological quality. Practical Applications Fish is a highly perishable commodity after harvesting and postmortem as a consequence of microbial breakdown mechanisms. Total viable count (TVC) method is the most widely used microbiological indicator for the evaluation of fish microbiological quality. However, the conventional analytical methods for the determination of TVC are cumbersome and time wasting. This work provides a practical and efficient way for rapid, accurate and convenient determination of TVC in fish using electronic tongue combined with regression algorithms to address these limitations.

A colorimetric sensor array is a rapid and high sensitivity sensor for the detection and identification of volatile organic compounds. Theoretical investigations are performed to study the binding ability of the colorimetric sensor array with volatile organic compounds. Cobalt-porphyrin is selected to investigate the binding ability of the colorimetric sensor array with small volatile organic compounds. The binding energy of cobalt-porphyrin with small volatile organic compounds, such as O 2 , N 2 , H 2 S, trimethylamine, propanol, propane, ethyl acetate, butanone and so on, is investigated using density functional theory (DFT) methods at three different spin multiplicities: low-spin (singlet), intermediate-spin (triplet) and high-spin (quintet) states. The relative and absolute binding energies of all the complexes are obtained at the optimized geometries. The triplet state is found to have the lowest energy for the CoP-O 2 complex, whereas the singlet state has the lowest energy for the other complexes. The binding energies for the complexes considered are in order starting from the lowest energy state: H 2 S < propane < O 2 < N 2 < ethyl acetate < butanone < propanol < trimethylamine. This theoretical result can be used to optimize the sensor to increase the detection ability of the colorimetric sensor array.

This article examined the potential of sensor fusion of near-infrared spectroscopy (NIRS) and electronic tongue (ET) together with multivariate analysis, for the accurate and rapid classification of five cocoa bean varieties. Optimum data extraction was done from each sensor and principal component analysis was used for data fusion by normalization. Support vector machine was used to develop the classification model. The model was optimized by cross-validation and assessed by the numbers of principal components (PCs) and the classification rate. The single sensors (NIRS and ET) has a classification rate between 83 and 93%, while, data fusion (ET-NIRS) had a classification rate of 100% at three PCs in both the training and prediction. Comparatively, the data fusion technique was superior to the single techniques. The findings could be exploited for reliable and rapid identification and discrimination of cocoa bean varieties. The study showed its novelty in the possibility of combining ET and NIRS data for accurate classification of cocoa bean varieties. PRACTICAL APPLICATIONCocoa processor will always demand high-quality cocoa beans and breeders are in the business of releasing new varieties. The analytical methods used to assess and differentiate varieties are cumbersome, time consuming, and require an elaborate sample preparation and chemical usage. Also, single-sensor technique does not also always provide accurate means to differentiate very similar varieties. This research investigated the feasibility of integrating near-infrared spectroscopy (NIRS) and electronic tongue (ET) together with multivariate calibration analysis for rapid classification of cocoa bean varieties. This research has demonstrated that data fusion of NIRS and ET together with support vector machine could be used to differentiate cocoa bean varieties. These findings would be very useful to cocoa-producing countries, processors, and quality assurance managers for overcoming mislabeling and adulteration. Breeders can also use this technique for rapid and easy differentiation of cocoa bean varieties to facilitate breeding process