PendahuluanPenentuan kematangan Tandan Buah Segar (TBS) sawit di perkebunan sangat mempengaruhi rendemen dalam produksi minyak sawit. Apabila minyak pada buah mentah atau lewat matang ikut diekstraksi, maka dapat menurunkan hasil rendemen. Salah satu upaya peningkatan rendemen minyak yaitu dengan dengan mengoptimalkan kegiatan penentuan kematangan buah, karena sampai saat ini masih menggunakan metode konvensional yaitu dengan melihat secara visual. TBS dikatakan layak panen apabila sudah menjatuhkan brondol (buah kecil) sebanyak 10-15 butir. Kenyataan di lapangan, terdapat TBS yang sulit menjatuhkan brondol atau brondol tersangkut di sela pelepah. Penentuan secara konvensional ini juga sangat bergantung pada pengalaman, kondisi psikis serta pengetahuan pemanen saat menentukan kematangan buah. Technical Paper Pendugaan Kadar Air dan Total Karoten Tandan Buah Segar (TBS) Kelapa Sawit Menggunakan NIR Spektroskopi Keywords: Fresh Fruit Bunch (FFB) maturity, NIR spectra, calibration model, water content, total carotene AbstrakTujuan dari penelitian ini adalah untuk membangun model kalibrasi dari kadar air dan total karotenyang dapat dijadikan standar kematangan buah. Terdapat tiga tahapan pada penelitian ini, pertama akuisisi spektrum Near Infrared (NIR) pada 60 sampel menggunakan NIRFlex N-500. Langkah selanjutnya adalah pengujiankadar air dan total karoten tiap sampel secara destruktif. Langkah terakhir adalah pembuatan model kalibrasi menggunakan metode (Partial Least Square) PLS dan menerapkan pretreatment data Standard Normal Variate (SNV), normalisasi (N01), dan First Derivative Savitzky-Golay 9 titik (DG1). Hasil menunjukkan bahwa kadar air dapat diprediksi dengan baik menggunakan SNV dengan R 2 (kalibrasi) = 0.89 dan R 2 (validasi) = 0.88 dan RPD = 2.84. Total karoten juga dapat diprediksi dengan baik menggunakan DG1 dengan R 2 (kalibrasi) = 0.84 dan R 2 (validasi) = 0.77 dan RPD = 2.06.
Kadar Asam Lemak Bebas (ALB) yang rendah merupakan salah satu indikator kualitas Crude Palm Oil (CPO) yang baik. Apabila Tandan Buah Segar (TBS) kelapa sawit yang lewat matang ikut diolah menjadi CPO, maka kadar ALB selama produksi dapat meningkat. Proses pemanenan menjadi titik krusial yang sangat mempengaruhi tingkat kematangan buah. Selama ini penentuan kematangan TBS kelapa sawit masih dilakukan secara visual yang bergantung kepada kemampuan dan kondisi pemanen buah sawit. Oleh karena itu, perlu dikembangkan suatu metode secara kuantitatif yang dapat memprediksi kadar ALB secara objektif. Pada penelitian ini, akan dikembangankan metode non-destruktif berbasis NIR spectroscopy yang akan dikaji sebagai metode untuk menentukan tingkat kematangan TBS berdasarkan kandungan ALB. Penelitian ini dibagi menjadi tiga tahapan, yaitu akuisisi data reflektansi spektrum TBS dengan NIR Flex N-500, pengukuran kadar ALB, dan pembangunan model kalibrasi dengan menggunakan kemometrik. Dari hasil pengembangan model didapatkan nilai R2 tanpa preprocessing sebesar 0.236, RPD sebesar 1.27 dan PC sebesar 2. Proprocessing First Derivative Savitzky Golay (DG1) memberikan nilai koefisien determinasi tertinggi yaitu sebesar 0.243, dengan nilai RPD sebesar 1.17 dan PC sebesar 2. Akan tetapi kualitas model kalibrasi yang dibangun tetap belum mampu menunjukkan kehandalan dalam memprediksi kandungan ALB tandan buah segar kelapa sawit.
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.
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.
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.
Basal Stem Rot (BSR) disease was considered as the most destructive disease in oil palm tree. Ganoderma boninense. fungi causing BSR on oil palm tree, could release cell wall degrading enzymes (CWDE) which responsible to degrade polysaccharide on oil palm tree cell wall such as cellulose and lignin into reducing sugar. In this research optical measurement using UV/Vis Diffuse Reflectance Spectroscopy (DRS) was utilized to qualify and quantify BSR level based on its reducing sugar. Several stages were conducted to qualify and quantify reducing sugar comprising sample preparation, spectral data acquisition and chemometrics analysis. Health Stem (HS) and Infected Stem (IS) were prepared. The two stem condition were dried and powdered separately then mixed into 5 mixtures from both stems (100% HS, 75% HS + 25% IS + 50% HS + 50% IS, 25% HS +75% IS and 100% IS) and duplicated to produce 10 total sample. Reducing sugar was measured for each sample. Other than that, spectrum acquisition data was conducted using UV/Vis DRS. In the final stage, chemometrics analysis was performed where reducing sugar was set as response and spectral data was set as predictor. The result showed that Principal Component Analysis (PCA) could well classify 4 group of mixtures. While for quantitative analysis, Partial Least Square (PLS) and Support Vector Machine Regresion (SVR) were used to develop prediction model of reducing sugar. PLS showed low performance with the highest R 2 val and R 2 cal accounting for 0.218 and 0.019 and RMSEC and RMSECV were 0.019 and 0.023, respectively. Besides, SVMR using first derivative savitzky-golay (DG1) preprocessing showed high R 2 cal and R 2 val accounting for 0.823 and 0.701 with RMSEC and RMSECV were 0.012 and 0.028, respectively.
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