No abstract

Traditional fermentation process usually takes a long time and hard to achieve and maintain the optimum temperatures condition. The controlled fermentation process can be done using a fermentor for conditioning the temperature at the beginning of the process as bacteria and yeast can develop optimally at certain temperatures. However, traditional heaters that utilize heat transfer by conduction and convection have not been able to homogenize the temperature of products so that the fermentation process is not well controlled. Heating technology that has great potential to produce fast and uniform heating is by using ohmic technology. This research aims to design and to conduct a test of ohmic-based fermentor model. Methods used in this study were a functional and structural analysis of fermentor design and performance test of the fermentor model. The test was conducted using two materials as the heated product (0.02 M NaCl solution as a liquid product and soybean as a non-liquid product) with three variations of voltage treatment (110, 165, and 220 V). Design of the ohmic-based fermentor model had been constructed and tested. Analysis of alternative methods, types, and ohmic fermentor material has been carried out as a reference in designing. Test results showed that the fermentor had been proven to be able to maintain, control, and monitor the temperature in the reactor in real-time. The energy efficiency achieved a high value of 81.96% up to 86.29%. The temperature distribution in the fermentor was also determined uniform for both liquid and non-liquid products with the average value of 40.01±0.16°C (the reference temperature is 40°C). It was indicated that the fermentor model can be used for further fermentation test using various products.

Moisture content in the process of drying is often unknown when carrying out the drying process, especially the fluidized dryer. A lot of experimental designs are needed when observing the drying phenomenon more deeply.  It is because to stop and repeat drying process from the beginning again when the sample is taken to test its moisture content needed more experiments. Therefore, this paper presents development of a non-intrusive moisture measurement system prepared for fluidization type dryers. The method used in to conduct this research consists of (i) structural design analysis and (ii) functional (mechanical and electrical systems) and (iii) simple testing of the water content measurement system of constructed material. Test parameters observed include errors in measuring and fluctuating sensor signals against vibration applied to the weighing system. The results showed that non-intrusive moisture content measurement system for fluidized dryers based on the ESP8266 microcontroller had been successfully developed and worked normally. The measurement system has been calibrated with a coefficient of determination (R2) close to one. Measurement error resulting from the effect of vibration on this system shows a very satisfactory value of 6.89%.

This paper presents the spectroscopic dataset, pre-processing, calibration, and predicted model database of Fourier transform infrared (FTIR) spectroscopy used to detect adulterated coconut milk with water. Absorbance spectral data were acquired and recorded in wavelength range from 2500 to 4000 nm for a total of 43 coconut milk samples. Coconut milk ware prepared in three forms of adulteration. Coconut milk comes from traditional markets and instant coconut milk in Indonesia. Spectra data may also be pre-processed to increase prediction accuracy, robustness performance using normalize, multiplicative scatter correction (MSC), standard normal variate (SNV), 1st derivative, 2nd derivative, and combination of 1st derivative and MSC. Calibration models and cross-validation to forecast those adulteration parameters use two regression algorithms, i.e., principal component regression (PCR) and partial least square regression (PLSR). By looking at its statistical metrics, prediction efficiency can be measured and justified (correlation coefficient (r), correlation of determination (R 2 ), and root mean square error (RMSE)). Obtained FTIR datasets and models can be used as a non-invasive method to predict and determine adulteration on coconut milk.

The contamination of agricultural products, such as vegetables, by pesticide residues has received considerable attention worldwide. Pesticide residue on vegetables constitutes a potential risk to human health. In this study, we combined near infrared (NIR) spectroscopy with machine learning algorithms, including partial least-squares discrimination analysis (PLS-DA), support vector machine (SVM), artificial neural network (ANN), and principal component artificial neural network (PC-ANN), to identify pesticide residue (chlorpyrifos) on bok choi. The experimental set comprised 120 bok choi samples obtained from two small greenhouses that were cultivated separately. We performed pesticide and pesticide-free treatments with 60 samples in each group. The vegetables for pesticide treatment were fortified with 2 mL/L of chlorpyrifos 40% EC residue. We connected a commercial portable NIR spectrometer with a wavelength range of 908–1676 nm to a small single-board computer. We analyzed the pesticide residue on bok choi using UV spectrophotometry. The most accurate model correctly classified 100% of the samples used in the calibration set in terms of the content of chlorpyrifos residue on samples using SVM and PC-ANN with raw data spectra. Thus, we tested the model using an unknown dataset of 40 samples to verify the robustness of the model, which produced a satisfactory F1-score (100%). We concluded that the proposed portable NIR spectrometer coupled with machine learning approaches (PLS-DA, SVM, and PC-ANN) is appropriate for the detection of chlorpyrifos residue on bok choi.