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This paper will report the feasibility study on the use of UV/Vis spectroscopy to determine apple quality based on Total Phenolic Compound (TPC) and pH. To achieve the conclusion, several stages had to be conducted. First, the total 50 sample of apples from 4 different ripening stages (3, 4, 5 and 6 month) were collected from local farmer in Bumiaji District and extracted. The juice from extraction was prepared for UV/Vis spectrum collection ranging from 200-1100 nm. TPC and pH were then measured. Afterwards, chemometrics analysis was performed to provide Partial Least Square (PLS) prediction model. And lastly, was identifying robustness of the model by analysing all the statistic parameter. The result showed that, partial spectrum of PLS model to predict absorbance of TPC provided good determination coefficient (R2 calibration was 0.803 and R2 validation was 0.710) while RMSEC and RMSECV were 0.070 and 0.088 respectively. As for pH model prediction, the best model was also obtained from the partial spectrum resulting R2 calibration was 0.822 and R2 validation was 0.797 while RMSEC and RMSECV were 0.56 and 0.61, respectively.

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.

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.

In order to produce shelled groudnut, local merchants in Tuban are commonly use the manual methods by hand to shelled the groundnut which ineffective and inefficient. Therefore, the aim of this research is to design a groundnut shelling machine to accelerate the kernel production. There were three main stages that were conducted to achieve the result: designing, manufacturing and performance examination. Solidworks 2016 was used to design the 3D model of the machine before the manufacturing process in a workshop. The functional part of the machine consists of serrated roller and stator. While stator holds the groundnut, the roller rotates to break the pod by mechanical contact. Performance of the machine was then examined by varying the rotation speed of motor: SR 1, SR 2 and SR 3. One-way ANOVA perceived that SR 1 gave the significant fastest average operation (115.46 kg/h) (p < 0.0001) followed by SR 3 (96.78 kg/h) and SR 3 (99.89 kg/h). Speed Rotation 1 also gave the lowest damaged kernel about 30.96% among all dim (p < 0.05). Moreover, the machine gave more than 99% shelling efficiency and less than 0.5% losses. Further research must be conducted regarding varying lower rotation speed, moisture content, and different variety of groundnut.

In Indonesia, green grass jelly is widely known as traditional drink obtained from soaking grass jelly leaves in the water. Nowadays, the production process of grass jelly was conducted manually, which consumes lots of time and energy. Therefore, this research aimed to design a semi-automatic green grass jelly squeezer to accelerate and simplify the production process. Moreover, the squeezer performance and several quality parameters of the jelly produced by the squeezer were examined. The designing process of green grass jelly squeezer was conducted through several stages. Those were concept design, manufacture and assembled stage, performance test, modification, and examination. The result shows that the most efficient production process was at 6000 RPM and the digital value of 540. The measurements of Soluble Solid (SS) and gel strength show that jelly produced by the squeezer has higher SS and F max than the control. The sensory evaluation shows jelly with the digital value of 520 get the best consumer acceptance, which means the consumer prefer neither too dense nor too solid green grass jelly. The result shows that no effect of digital value and RPM on syneresis examination.