Determination of the myristicin and alpha-pinene content of nutmeg is still constrained by the extended testing time in the laboratory, which is expensive and is carried out destructively. In addition, non-destructive testing using spectroscopy often faces problems in building models that only rely on algorithms that perform linearly, such as PCR and PLSR. Therefore, the present study studied Vis-NIR (381-1065 nm) as a fast, inexpensive, and non-destructive mechanism to determine the myristicin and alpha-pinene of nutmeg fruits from Aceh, Indonesia. Two algorithms commonly used in spectral data processing, partial least squares regression (PLSR) and machine learning represented by a support vector machine (SVM), were employed and compared to predict myristicin and alpha-pinene in nutmeg fruits. The chemical reference parameters (myristicin and alpha-pinene) were measured using gas chromatography mass spectrometry (GC-MS). Standard normal variate (SNV) and multiplicative scatter correction (MSC) preprocessing were involved as spectra enhancement before the prediction models outcome. The results show that the kernel of the radial basis function (RBF) kernel n-SVM algorithm is better than PLSR for myristicin prediction with gamma (g), c, and nu (n) of 0.1, 1.0, and 0.99, respectively. Also, the e-SVM algorithm by RBF kernel is better than PLSR for the prediction of alpha-pinene in nutmeg fruits with gamma (g), c, and epsilon (e) compositions of 0.01, 10, 0.1, respectively. The coefficient correlation of calibration (rc) and coefficient determination of prediction (Rp2), the root means square error of calibration (RMSEC) and prediction (RMSEP), and the ratio (RPD) for the prediction of myristicin were 0.992, 0.986, 0.941%, 1.325% and 8.348, respectively. The coefficient correlation of calibration (rc) and coefficient determination of prediction (Rp2), the root mean square error of calibration (RMSEC) and prediction (RMSEP), and the ratio of prediction to deviation ratio (RPD) for the prediction of alpha-pinene were 0.976, 0.979, 0.305%, 0.317% and 6.826, respectively. In general, the results satisfactorily indicate that Vis-NIRS, with the appropriate algorithm, has promising results in determining myristicin and alpha-pinene on nutmeg from Aceh, Indonesia, as nondestructive measurement.

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The use of renewable energy such as biogas is a must to support the achievement of the targets that have been echoed through the go green campaign, namely public awareness of the clean air environment. The power of the otto cycle combustion engine is highly dependent on the conversion and heat of combustion (% CH4 in biogas) as well as the stoichiometric fuel-air ratio requirements. A performance test of the otto cycle motor fueled with biogas with various methane contents has been carried out. It was found that biogas with 95% CH4 at 3000 rpm produces the highest power of 3.906 kW, compared to the power of gasoline, but with a 4.8% decrease in power. Meanwhile, the average power produced from biogas with 55% CH4 content is 2,296 kW. The average torque of the biogas engine (95% CH4) is 7,609 (N.m) and the highest is 9,014 (N.m), but still 24% lower than the gasoline engine. The most efficient operation of the engine fueled with biogas was observed at 1,831 L/h biogas consumption as opposed to 1.4 L/h when the engines was run using gasoline. In other words, a lower % of CH4 was attained with a x% increase in fuel consumption. The average specific fuel consumption (SFC) of biogas (95% CH4) is 0.717 kg/kW.h, on the other hand, the gasoline engine is 0.395 kg/kW.h. The highest thermal efficiency of a biogas engine attained at maximum loading can match the efficiency of an ideal gasoline engine, which is close to 25%.

A helical barrier as air-biogas mixing device was designed and tested for direct use of biogas from digester in otto cycle generator set. Homogeneity of the air-fuel mixture can give better combustion reaction and increase engine power. The design was based on simulation, which shows that a 0.039 m length of helical barrier gave a 5% increase in power compared to non-helical barrier. Likewise, the simulations also showed that the helical barrier reduced specific fuel consumption (SFC) by 8%. Accordingly, the mixer with helical barrier was designed, and fabricated. Its performance test confirms the improvement resulted by using helical barriers as air-biogas mixer in the engine. The experiment showed that the power increased by 5% when using helical barrier, while SFC decreased by 4.5%. It is concluded that the helical barrier can increase the homogeneity of the mixture resulting in better engine performance. Besides, emissions produced from the engine using a helical barrier also decreased.