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This study aims to obtain the high-quality near-infrared (NIR) spectra of fresh corn cobs. A systematic investigation was made to explore the effects of experimental parameters on the spectral features of fresh corn cobs and modeling validation using NIR diffuse reflectance spectroscopy. According to the cob stick-shaped characteristics, the multi-dimensional comprehensive test was carried out to collect 900~1700 nm spectral data under four parameters, namely, light source type, light intensity, detection distance, and light source angle. The halogen lamp cups were used as the fiber optic light sources. Among them, the halogen lamp cups were selected with the power of 20 and 40 W, detection distances of 10 and 50 mm, and halogen lamp cups with angles of 30°, 45°, and 60°, in order to analyze the spectral differences and distribution patterns of curves. The spectral differences were determined for the distribution patterns. The standard deviation and spectral area extreme deviation indexes were used to evaluate the spectral quality. Further validation tests were carried out on the model. The spectra were evaluated at 30° and 45° halogen lamp cup angles by multiplicative scatter correction (MSC), standard normal variate (SNV), first derivative (FD), and trend correction. Furthermore, the new model was established to predict the water content using partial least squares (PLS) and support vector machines (SVM). The performance of the model was compared after the derivative (FD) and detrending (DT) pre-processing. The experimental results showed that sufficient spectral response and less interference were achieved in the halogen lamp cup with a power of 20 W and a detection distance of 40 mm. The standard deviation and spectral area polarity of the curve were 0.83 and 187.2, respectively. The better quality of the spectral curve and higher performance of the model were also obtained in the halogen lamp cup with the clamp angle of 45°, compared with 30°. The better performance was found in the SVM prediction model after SNV preprocessing. The coefficients of determination were 0.943 and 0.880, respectively, in the correction and prediction datasets, while the root mean square errors were 0.708 and 0.932, respectively, and the residual prediction deviation was 2.956. The finding can provide technical support to the nondestructive test on the intrinsic quality of fresh corn cobs using near-infrared diffuse reflectance spectroscopy. [ABSTRACT FROM AUTHOR]

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Copyright of Packaging & Food Machinery is the property of Packaging & Food Machinery Magazine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the multi-channel transient signal test, the redundant data test by the conventional test system at long time and high sampling rate causes the storage and transmission problems. An adaptive frequency-change sampling strategy is presented to solve these problems. The algorithm lets the test system acquire the primary data at the same sample rate as the traditional test systems can, and divides the data in time. According to the frequency domain characteristics of signal in each segment, the occurring time and duration of transient signal are determined. The non-transient signals are resampled to reduce the redundant data, and the transient segments can be saved completely. The data compression ratio is more than 33 after the data is processed by the algorithm, while the duration data of transient signal is fully retained, and the maximum error of the non-transient segments are less than 0.5FS. The experimental results show that the algorithm can effectively reduce the redundant data in transient test. [ABSTRACT FROM AUTHOR]

In the process of shock wave testing, the test signal is distorted by the limited bandwidth and low resonant frequency of pressure sensor. In order to solve this problem, a pressure sensor dynamic compensation method which is based on fireworks algorithm is presented. And the fitness function for the dynamic compensation is improved to increase the dynamic performance. The transfer function of dynamic compensation of pressure sensor is obtained from the test data of shock tube. The rise time of step pressure signal can be improved to 15.0 μs, and the overshoot is reduced to 8.27% after dynamic compensation by the proposed method. In the actual muzzle shock wave test, the dynamic compensation method can suppress the resonant frequency effectively, and make the pressure sensor test system achieve a better dynamic performance. [ABSTRACT FROM AUTHOR]