Search

Testing the antioxidant activity of chili powder is often destructive; these methods are expensive, complicated, and lengthy analysis time. Meanwhile, information on antioxidant activity is needed by the industry to determine its quality class in rapid and uncomplicated handling. Therefore, this study was aimed to measure the antioxidant activity of chili powder and classify it into three quality classes, namely high, medium, and low, using Near Infrared (NIR) spectroscopy at spectral wavelengths of 1000-2500 nm and combined with chemometric techniques. The antioxidant activity of the sample was evaluated using the DPPH assay. Processing of the data started with outlier detection using Hotelling's T2 , then confirmed using leverage analysis and influence plot. The data were then processed with Smoothing-Savitzky Golay, SNV, and De-Trending. Principal Component Analysis (PCA) was performed for classifying the samples, which were validated with full crossvalidation. The results showed that the antioxidant activity was detected at a range of 1395 - 2390 nm. De-Trending was the best pre-treatment that successfully classified the low and high levels of antioxidant activity with a success rate of 100% and classified a medium level of antioxidant activity with a success rate of 96.97%.

The objectives of this study were to confirm the application of automatic system for detecting the water stress and the compatibility of the water stress treatment system for producing high soluble solids tomato fruits in high technology greenhouse. The tomato variety used, “Rinka 409”, was grown hydroponically with a high-wire system in high technology greenhouse in Faculty of Agriculture, Ehime University, Japan. The greenhouse has a full controlling system (temperature, relative humidity, carbon dioxide, and light intensity) to support the seasonal change and was adjusted to the real-time condition. Water stress condition of the tomato plant in the high technology greenhouse was detected by measuring the water content in the rockwool slab and by using visual monitoring system. The result showed that the automatically nutrient supplying system using visual monitoring system based on speaking plant approach technology could be used for the detection of water stress in the tomato plant. Using this system, water stress tomato cultivation was detected and the high soluble solids tomato fruits could be produced continuously to fulfill consumer expectation.

Chillies are a high-value export commodity. One of the chilli products that has a high export value, such as to the European market, is dried red chillies. Dried chillies’ quality is the main parameter that must be maintained if this commodity is to be continuously exported. This study aimed to precisely and accurately classify the quality of dried chillies using physical parameters of the length and color of chillies based on digital image processing. In this research, the quality of dried red chillies was classified using a combination of digital image processing and artificial neural networks (ANN). The quality parameters of dried chillies used as inputs were chilli length, mean energy, mean a*, mean blue, and mean contrast. This study used 150 dried chillies for training data and 36 samples for test data. The classification of the quality of dried red chilli was divided into three classes, which were extra class, class I, and class II. The result of this study was an artificial neural network structure consisting of 5 input layer cells, 16 hidden layer cells, and two output layer cells. The testing of the system using 36 testing data that determined the values of dried chillies reached 94.4%.

This study aims to determine the image parameters that can the chili quality and arrange the best Artificial Neural Networks (ANN) architecture in order to classify the quality into several groups. The image was taken and analyzed by means of image processing and ANN. Samples of chilies were divided into 2 groups, 75% for training data and 25% for network testing. The sample was placed in a black box and the image was captured using smartphone camera for both sides. The sample image was converted to binary to get the color value. Value color of image processing results using Matlab were used as input parameters for color group identification of ANN. Subsequently, color used as variable for classification using ‘if then’ logic. The results showed that the color elements of the image that could be the quality identity of chili were red, green, a*, and intensity. The ANN input layer that consisted of 4 input cells, 9 hidden layer cells, and 1 output layer cell could classify chilies into 4 color groups i.e.green, tinge of red, red and dark red with an accuracy of 90.43%.