In this research, an industrially important enzyme tannase and product gallic acid was produced with an inexpensive novel substrate Swietenia macrophylla. Fermentation of S. macrophylla was optimized using a two-step approach: First, the traditional One variable at-a-time technique, and second, the statistical Box-Behnken design for co-production of tannase enzyme and gallic acid. This two-step method of optimization showed the highest tannase activity and gallic acid yield of 0.0497 U/mL and 225 µg/mL respectively which is a 29.5 and 49-fold increase when compared to unoptimized conditions. Further, the partially purified tannase enzyme was characterized and showed optimal tannase activity at pH 4.0 and 30 ℃, and was stable between pH 3.0–6.0 and 4−40 ℃ for 24 h and 10 h, respectively. Also, metal ions such as Ca2+, Na+, and K+ at 1 mM concentration; and organic solvents methanol, and isoamyl alcohol at 20% v/v exhibited the highest activity at optimized reaction conditions. Whereas, Mn2+, Zn2+, Mg2+, Fe2+ and Fe3+, EDTA, TritonX 100, toluene, and hexane caused the tannase inhibition at higher concentrations. In the end, the fermentative production of gallic acid was verified qualitatively through thin-layer chromatography and Fourier transform infrared spectroscopy. Graphical Abstract

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Soil texture using a hydrometer or pipette method requires expertise, although these are accurate. A soil expert may help the farmer to detect the soil texture by analyzing the visual texture of the soil, which is not always accurate. This paper presents the smartphone image-based sand and clay soil classification in wet and dry humid conditions using Self Convolution Neural Network (SCNN) and finetuned MobileNet.A soil dataset of 576 soil images was prepared using a low-cost smartphone under natural light conditions. Different augmentation techniques such as shift, range, rotation, and zoom were applied to the soil dataset to increase the number of images in the soil dataset. The best performance of the MobileNet was reported at epoch 15 with a testing and training loss of 0.0091 and 0.0194, respectively. Though the SCNN model performed best at epoch 10 with a testing accuracy of 99.85%, the MobileNet reported less computation time (167.8s) than the SCNN (273.2s). The precision and recall of the models were 99.62 (MobileNet) and 99.84 (SCNN). The accuracy of the SCNN reported itself as the best model, whereas the computing time of the MobileNet reported itself as the best model in different humid conditions. The model can be used to replicate the traditional soil texture analysis method and the farmers can use it for better productivity.

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