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Soluble solids content (SSC) is one of the most important quality attributes affecting the taste and maturity of fresh fruit. In this study, with the cerasus humilis fruit as the research object, a prediction model of soluble solid content (SSC) in cerasus humilis (CH) is established based on visible / near-infrared spectroscopy to explore a nondestructive testing method of the interior quality of CH. The visible / nearinfrared spectral info (350-2500nm) of 160 CHs was collected to extract the reflection spectrum, establishing the linear model (PLSR) and non-linear model (LS-SVM) of CH’s spectral info and SSC. The prediction performance and stability of the model were justified using several statistical indicators namely correlation coefficient of the prediction set (Rp), the root mean square error of the prediction set (RMSEP), and the residual predictive deviation (RPD) index. Results showed that multiplicative scatter correction (MSC) was proved to be the best preprocessing method, UVE-CARS was the optimal method of dimension reduction, the quantities of characteristic wavelengths was 10 and the optimal model was UVE-CARS-PLSR, in which Rc is 0.8995, Rp is 0.8579, RMSEC is 0.8897, RMSEP is 0.9059, and RPD is 1.8766, indicating that the redundant data of the original spectrum can be reduced, the wavelength dimensions can be reduced, valid info can be retained and data processing can be simplified as UVE-CARS extracts characteristic wavelengths. Reference and theoretical basis are provided in this research for future research and development of portable detector and online sorting detection of CH internal quality. 摘要 可溶性固形物含量(SSC)是评价鲜果口感和成熟度的重要品质指标之一。本研究以欧李果实为研究对 象,基于可见/近红外光谱技术结合化学计量学方法建立欧李果中 SSC 含量的预测模型,探究欧李果内部品质 的快速无损伤检测方法。采集 160 个欧李果的可见/近红外光谱信息(350~2500nm),建立欧李果光谱信息 和 SSC 的线性模型(偏最小二乘回归算法)和非线性模型(最小二乘支持向量机)预测模型,通过预测集相关系数 (Rp)、预测集均方根误差(RMSEP)和剩余预测偏差(RPD)等指标来评价模型的预测性能及稳定性。结果表明, 多元散射校正为最佳预处理方法,最佳降维方法为 UVE-CARS,特征波长个数为 10,最优模型为 UVECARS-PLS,其中 Rp 为 0.8579,RMSEP 为 0.9059,RPD 为 1.8766。说明 UVE-CARS 提取特征波长可减 少原始光谱的冗长数据,降低波长维数,保留有效信息,简化数据处理。本研究为欧李果内部品质后续便携式 检测仪和在线分选检测研究提供了参考和理论基础。 INTRODUCTION Cerasus humilis (Bge.) Sok. (CH) is a kind of rosaceae cherry. It is usually grown in sun-slope sandy land and mountain shrubs, or cultivated in gardens. The fruit ripens around August 20 every year. CH is usually found in the northern regions of Yellow River. CH has a strong reticular root system and is droughtresistant, making it not only fixate soil but also regulate ecosystem, and it is also known as the “fruit rich in calcium” for its high calcium content, and is the third generation exclusive in China. CH’s seed kernels are the main source of Yu Li Ren (Semen Pruni), an herb known for the idea of “homology of medicine and 1 Bin Wang, As. Ph.D. Stud. Eng.; Junlin He, Prof. Ph.D. Eng.; Shujuan Zhang, Prof. Ph.D. Eng.; 2 Lili Li, As. Ph.D. Stud. Eng. Vol. 61, No. 2 / 2020 INMATEH – 252 food”. CH pulp can be consumed or made into juice, fruit wine, vinegar and preserved dried fruits, and it is good for health. The CH products have a unique taste and rich aroma and of high nutritional value, so it is known as a “super fruit”, and honoured as one of the three high-end fruits with American blueberry and Russian sea-buckthorn. The internal qualities of CH are abundant. Soluble solid content (SSC) and titratable acidity (TA) will affect its taste and nutrition when it comes to its quality assessment, and they serve as the measurement standards and important indicators of CH’s maturity. Regular testing of CH’s internal qualities is destructive, complicated and time-consuming, and its tissues are often damaged, affecting its sales and edibility and lowering its values (Guo et al., 2010). Near-internal spectroscopy has been successfully applied by domestic and overseas researchers to the fruit internal quality determination in recent years. There are in-depth studies on apples, mangos, tomatoes, kiwis, peaches, pears, strawberries and fresh dates. Ar et al., (2019) used near-infrared spectroscopy to predict the possibilities of internal qualities of persimmons such as SSC, Vc, total acid and hardness. The results showed that MSC is the optimal preprocessing method. With the establishment of a PLS calibration model, the optimal factor quantity of the SSC, Vc, total acid and hardness of persimmons is 17, 16, 12 and 12, respectively. Parpinello et al., (2013) studied a detection method, which combines nearinfrared ray (NIR) measurement and glucose analysis, and the partial least squares (PLS) model based on cross-validation serves as the main statistical parameter where the prediction set determination coefficient is 0.82 and the standard error of prediction is 0.83%°Brix. Purwanto et al., (2015) used near-infrared spectroscopy to predict the SSC and acidity of the mango species known as “Gedong Gincu”. The results showed that different preprocessing methods play critical roles in terms of establishing accurate models of mango internal quality prediction. Maniwara et al., (2014) used visible light and short-wave near-infrared spectroscopy to establish a PLSR prediction model for the indicators of soluble solids content, titratable acid content, ascorbic acid content, ethanol concentration, and peel hardness of passion fruit. Studies showed that PLSR prediction model proves to have the best prediction performance on the SSC in passion fruit, and the prediction correlation coefficient is 0.923. Escribano et al., (2017) collected the NI spectra of sweet cherries within the wavelength of 729 to 975 nm, and established a PLS prediction model of SSC for sweet cherries under two temperature conditions. The results showed that the determination coefficient (R2) of SSC calibration set is 0.922 and 0.946 and the standard error is 0.612% and 0.792% when the temperature is 0C and 23C, respectively. Yu et al., (2017) tested the SSC in grapes based on NIR and RC, RMSEP and RMSEC of PLS are 0.83, 0.76 and 0.84 by using the orthogonal test. Sun et al., (2018) used visible / nearinfrared semi-transmission spectroscopy to explore the influence of being unpeeled (complete) and peeled on the SSC detection accuracy of navel oranges. Studies showed that peel imposes significant impact on the SSC detection accuracy under the 5% confidence level. The correlation coefficient and root mean square error of the prediction set of the optimal PLS of the SSC in unpeeled and peeled navel oranges are 0.888 and 0.456% / 0.944 and 0.324%, respectively. Zhang et al., (2011) established the relations among visible light, near-infrared diffuse reflection spectrum (Vis/NIR) and the soluble tannin content in persimmons. The results showed that first derivative and detrending algorithms are the optimal preprocessing method, and the modified partial least squares (MPLS) demonstrated better prediction performance of the soluble tannin content in astringent persimmon, of which the RCV, RP2, RMSECV and RMSEP are 0.7227, 0.6785, 0.148 and 0.1763, respectively. The studies above show that it is feasible to use NIR to detect the internal qualities of fruits, but there is no research on the internal quality detection of CHs based on Vis/NIR spectroscopy. With cerasus humilis “Nongda 5” as the research object, a prediction model of the SSC in Cerasus Humilis (CH) is established based on visible / near-infrared spectroscopy to explore a nondestructive testing method of the internal quality of CH, hoping to achieve a CH SSC prediction model of good stability and high prediction accuracy. ASD Field Spee3 spectrometer has been employed to collect DRS data, and a spectral preprocessing method has been selected optimally. Four dimension-reducing methods, which are PLSR regression coefficients (RC), competitive adaptive reweighted sampling (CARS), and successive projections algorithm (SPA) and uninformative variable elimination (UVE), are used to extract the characteristic wavelengths, and different prediction models are built combined with PLSR and LS-SVM. They provide technical support for rapid, nondestructive, low-cost, and large-scale grading detection research of CH qualities. Vol. 61, No. 2 / 2020 INMATEH – 253 MATERIALS AND METHODS Cerasus humilis samples Sampling was conducted on August 15, 2019. The sampling site was the Jinzhong Agricultural HighTech Industrial Demonstration Zone Base in Taigu County, Shanxi Province, China (112°29'E, 37°23'N), and the variety was “Nongda 5”, sample growth state as shown in Fig. 1. Samples are of consistent maturity, shape and have no damage in order to minimize the influence of individual differences on experiment results. They were placed in a low-temperature fresh-keeping box which was transported to the laboratory the same day. The surface was wiped. Before the data acquisition, all samples were individually numbered and they were placed in an environment where temperature is 25°C and relative humidity is 20% for 6 hours to prevent the temperature from affecting the spectra and qualities. Fig. 1 Growth state of cerasus humilis A high-precision electronic balance (FA1004N, Shanghai) and a Vernier Caliper (Mitutoyo, Japan) were used to weigh and measure the weight and diameters of each sample. Table 1 shows the statistics of 160 samples. Table 1 Statistics of 160 samples Sample parameters Min. Max. Mean Standard deviation Variable coefficient (%) Diameter [mm] 15.48 28.66 24.72 3.62 14.64 Weight [g] 7.47 16.18 11.39 1.76 15.45 Vis/NIR collection A FieldSpec3 analytical spectral device (ASD, USA) was used to collect the VIS/NIR data of samples. The interval of spectral data is 1 nm, the number of scans is 30 times, the resolution is 3.5 nm, and the wavelength range is 350-2500 nm. Diffuse reflection was employed to sample the spectra. Each sample was scanned 3 times at an interval of 120 degrees above the equator, and its average value was taken as the final spectral data. The spectral experiment plat

inmateh - agricultural engineering

NONDESTRUCTIVE TESTING OF SOLUBLE SOLIDS CONTENT IN CERASUS HUMILIS USING VISIBLE / NEAR-INFRARED SPECTROSCOPY COUPLED WITH WAVELENGTH SELECTION ALGORITHM