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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%.

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%.