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In order to determine the ripening stages of apple (Malus sylvestris L), local farmer in Malang still utilized a traditional method by examining its size and appearance. However, this method needs worker’s extensive experience causing a non-standard ripeness level that may lead to lower the quality of crop. Thus, to strive this problem, fast and quantitative prediction method need to be developed. UV/Vis spectroscopy has shown its capability to provide a robust prediction of several internal attributes such as, Soluble Solid Content (SSC) and moisture content. The aim of this research is to develop a Partial Least Square (PLS) regression to predict internal parameter contained in apple. Fifty sample of apples were taken from local plantation in Bumiaji district. There were 3 stages to complete the research: (1) spectral data acquisition ranging from wavelength 200nm-1100nm; (2) Psycochemical measurement (moisture content, SSC and firmness); (3) Performing PLS regression based on spectral data and internal parameters. The result showed that PLS model of firmness could provide promising result where R2 calibration and validation were 0.658 and 0.635 respectively. On the other hand, the model could not predict both moisture content and SSC resulting 0.57 and 0.308 for R2 calibration. Further enhancement needs to be addressed to the firmness model for improving its capability of prediction.

The aim of this research is to develop a Near Infrared (NIR) calibration model based on water content and total carotene that can be used as a standard of ripe fruit. There are three steps of the research. The first step is NIR spectral data aquisition of 60 samples by using NIRFlex N-500. Next step is measuring water content and total carotene from each sample using destructive method. The last step is development of NIR calibration model using (Partial Least Square) PLS and applying pretreatment data Standard Normal Variate (SNV), normalization (N01), dan First Derivative Savitzky-Golay 9 Point (DG1). The result show that water content could be predicted well by applying SNV with R2 (calibration) =0.89, R2 (validation) = 0.88 and RPD = 2.84. Total carotene also could be predicted well by applying DG1 with R2 (calibration) = 0.84, R2 (validation) = 0.77 and RPD = 2.06.

In order to develop a model for predicting the oil palm Fresh Fruit Bunch (FFB) ripeness, a rapid and non-destructive method such as NIR spectroscopy is utilized. This method has shown its capability to determine the quality of some crops by predicting their internal chemical contents. The objective of the research is to investigate the feasibility of NIR spectroscopy to predict water and oil content in FFB by developing a calibration model. Sixty samples of FFB were scanned by using NIRFlex N-500 spectrometer ranging from 1000 to 2500nm. Water and oil content of samples were measured after scanned. To develop a calibration model, Partial Least Square (PLS) Regression and pre-processing were conducted using Unscrambler X 10.3. The results showed that PLS performs well to establish a calibration model to predict water content using MSC pre-processing with r2, factor, RSMECV, and RPD are 0.93, 3, 5.24, and 2, respectively. On the other hand, PLS could not be used well for establishing oil content calibration model because the result did not meet statistic parameters. For laboratory measurement, the model could predict water content of FFB; but it was limited to samples taken from the same variety and plantation. However, NIR Spectroscopy proposed a promising method to detect the ripeness of oil palm FFB.