Variable selection for partial least squares analysis of soluble solids content in watermelon using near-infrared diffuse transmission technique

This work is focused on the variable selection in building the partial least squares (PLS) regression model of soluble solids content (SSC) that is used to evaluate quality grading of watermelon. The spectra were obtained by the near infrared (NIR) spectrometer with the device designed for on-line quality grading of watermelon and the spectra of 680–950 nm were adopted to analysis. The variable selection was based on Monte-Carlo uninformative variable elimination (MC-UVE) and genetic algorithm (GA). In comparison of the performances of the full-spectra (680–950 nm) PLS regression model and the feature wavelengths PLS regression model showed that the MC-UVE–GA–PLS model with baseline offset correction combined multiplicative scatter correction (MSC) pretreatment was much better and 14 variables in total were selected. The correlation coefficients between the predicted and actual SSC were 0.885 and 0.845, the root mean square errors were 0.562 °Brix and 0.574 °Brix for calibration and prediction set, respectively. This work can make a great contribution to the research of on-line quality grading for watermelon nondestructively.     

Nondestructive determination of pear internal quality indices by visible and near-infrared spectrometry

The relationships between the nondestructive visible and near-infrared (Vis-NIR) measurements and the internal quality indices of pear fruit were established, and the potential of Vis-NIR spectrometry technique was investigated for its ability to nondestructively measure soluble solids content (SSC) and firmness of intact pear fruit. Intact pear fruit were measured by diffuse reflectance Vis-NIR in 350–1800 nm range. In this study, calibration models relating Vis-NIR spectra to SSC and firmness were developed based on multi-linear regression (MLR), principal component regression (PCR) and partial least squares regression (PLSR) with respect to the logarithms of the reflectance reciprocal log(1/R), its first derivative D₁ log(1/R) and second derivative D₂ log(1/R). The best combination, based on the robust models and the prediction results, was PLSR method with respect to log(1/R) at equatorial position of pear fruit. The PLSR models for prediction samples resulted correlation coefficient (r_p) of 0.912 and 0.854, and root mean standard error of prediction (RMSEP) of 0.662°Brix and 1.232 N for SSC and firmness, respectively. The results indicate that Vis-NIR spectrometry technique could provide an accurate, reliable and nondestructive method for assessing the internal quality indices of intact pear fruit.     

Non-destructive internal quality assessment of “Hayward” kiwifruit by waveguide spectroscopy

Soluble solids content (SSC) and Magness-Taylor flesh firmness (MTf) of “Hayward” kiwifruits were non-destructively assessed by means of a waveguide, that houses the fruit, connected to a sweeper oscillator and a spectrum analyzer. A preliminary test was conducted with a plastic fruit filled with solutions with different SSC values in the frequency range from 2 to 20 GHz (with a step of 1 GHz). The best linear correlations (R² up to 0.987) between electric signals and SSC solutions in the above described test were found in the 2-3 GHz and 15-16 GHz steps. These steps were used for the dielectric measurements on kiwifruit samples during storage of 28 days at 14 °C. Partial least squares (PLS) regression were then used to predict SSC and MTf from these acquired spectra. In “test set” validation, PLS models showed R² values up to 0.804 (RMSE = 0.98 °Brix) and 0.806 (RMSE = 8.9 N) for the prediction of SSC and MTf, respectively.     

Nondestructive evaluation of jujube quality by visible and near-infrared spectroscopy

This study compared prediction ability of interactance, transmission measurements of visible and near-infrared (Vis/NIR) spectroscopy in detecting the soluble solids content (SSC) of jujubes. Calibration models relating Vis/NIR spectra to SSC were developed based on partial least squares regression (PLSR) with respect to the logarithms of the reciprocal absorbance (log (1/R)), its first and second derivatives (D₁log (1/R), D₂log (1/R)). The PLSR models for prediction samples resulted correlation coefficients (r_p) of 0.74-0.91 and root mean square error of prediction (RMSEP) of 2.018-3.200 °Brix for interactance; r_p of 0.63-0.73 and RMSEP of 3.517-3.863 °Brix for transmission, respectively. The results indicate that interactance displays an obvious advantage over transmission measurement.The reflectance measurement was used to access the discrimination potential in sorting external insect-infested jujubes from intact class. Stepwise discriminant analysis (SDA) was performed to identify the effective wavelengths that best discriminated the insect-infested jujubes from intact jujubes and to derive a discriminant function in classifying the jujubes showing external infestation and those that were free of infestation. The results showed that log (1/R) had better correct classification rate than D₁log (1/R), and D₂log (1/R) for classifying intact, insect-infested and stem-end classes.     

First steps towards the development of a non-destructive technique for the quality control of wine grapes during on-vine ripening and on arrival at the winery

NIR spectroscopy was used as a non-destructive technique for the assessment of changes in certain internal quality properties of wine grapes (Vitis vinifera L.) during on-vine ripening and at harvest. A total of 108 different wine grape samples were used to construct calibration models based on reference data and NIR spectral data, obtained using a commercially-available diode-array spectrophotometer (380-1700 nm). The feasibility of testing bunches of intact grapes was investigated and compared with more traditional methods of presentation, such as berries or must. Predictive models were constructed to quantify changes in soluble solid content (SSC, °Brix), reducing-sugar content (g/l), pH-value, titrable acidity (g/l tartaric acid), tartaric acid (g/l) and malic acid (g/l), these being the major parameters used to chart ripening. NIRS technology provided good precision for the bunch analysis mode assayed for SSC (r² = 0.89; SECV = 1.41 °Brix), for reducing-sugar content (r² = 0.87; SECV = 17.13 g/l) and for pH-value (r² = 0.69; SECV = 0.19). Models developed for testing other fruit acidity parameters yielded results sufficient to provide a screening tool to distinguish between low and high acidity values in intact grapes. Significantly, the results obtained with bunch presentation were similar to those obtained with berries and must, thus justifying further implementation of NIRS technology for the non-destructive analysis of quality properties both during on-vine ripening and on arrival at the winery. This method allows musts to be processed separately depending on initial grape quality, assessed with a single spectrum measurement and in a matter of seconds.     

Nondestructive determination of soluble solids content and pH in tomato juice using NIR transmittance spectroscopy

The potential of near-infrared (NIR) transmittance spectroscopy to nondestructively detect soluble solids content (SSC) and pH in tomato juices was investigated. A total of 200 tomato juice samples were used for NIR spectroscopy analysis at 800–2400 nm using an FT-NIR spectrometer. Multiplicative signal correction (MSC), and the first and second derivative were applied for pre-processing spectral data. The relationship between SSC, pH, and FT-NIR spectra of tomato juice were analyzed via partial least-squares (PLS) regression. PLS regression models were able to predict SSC and pH in tomato juices. The r _c, RMSEC, RMSEP, and RMSECV for SSC were 0.92, 0.0703°Brix, 0.150°Brix, and 0.138°Brix, respectively, whereas those values for pH were 0.90, 0.0333, 0.0316, and 0.0489, respectively. It is concluded that the combination of NIR transmittance spectroscopy and PLS methods can be used to provide a technique of convenient, versatile, and rapid analysis for SSC and pH in tomato juices.     

Non-destructive assessment of apricot fruit quality by portable visible-near infrared spectroscopy

The ability of portable Near Infrared Spectroscopy to determine apricot fruit quality has been studied. Calibration models allowing the determination of soluble solids content (SSC), total acidity (TA) and firmness (Fi) of apricots were carried out with variable precisions. Models were built for each variety and global models combining different varieties were attempted. SSC was determined with a root mean square error of cross-validation (RMSECV) comprised between 0.67 and 1.1 °Brix and R-values between 0.88 and 0.96. Concerning Fi, the accuracy of the prediction was variety dependant. These predictions were correct for the varieties Kioto and Harostar with RMSECV-values between 6.2% and 13% (R-values between 0.85 and 0.92) and unsatisfactory for Bergarouge (RMSECV = 24%). TA was predicted with RMSECV-values between 0.79 and 2.61 g 100 ml⁻¹ and R-values between 0.73 and 0.97. In a second application, near infrared spectra were used to classify apricot fruits according to their variety and colour intensity with correct efficiency. The results obtained in the present study showed that NIRS technology could be applicable to apricot quality and that such portable devices could help to obtain a complete follow-up of the fruits in orchards and during post-harvest.     

基于可见/近红外光谱和变量选择的脐橙可溶性固形物含量在线检测

为联合可见/近红外光谱技术和变量选择方法在线检测脐橙主要内部品质指标可溶性固形物(SSC),分别选定脐橙校正集和预测集样本141个和47个,脐橙运输速度为0.3m/s,利用USB4000微型光谱仪在线采集脐橙样本的可见/近红外光谱,先分别采用无信息变量消除(UVE)和遗传算法(GA)对650~950nm波段范围的波长变量进行预筛选,再分别利用竞争自适应重加权采样(CARS)及连续投影算法(SPA)对波长变量进一步筛选,并应用偏最小二乘(PLS)方法分别建立脐橙SSC的在线预测模型,并与原始光谱等建立的预测模型进行比较。结果表明,对于脐橙SSC,预筛选方法GA优于UVE方法,变量选择方法CARS优于SPA方法;GA-CARS及GA-SPA联合变量选择方法优于对应的单一变量选择方法CARS及SPA。在上述变量选择方法中,GA-CARS方法获得的结果最优,其所建立的脐橙SSC的PLS模型的校正集和预测集相关系数分别为0.933和0.824,校正集和预测集均方根误差分别为0.429%和0.670%,性能优于原始光谱建立的PLS模型,且建模波长变量数由1 385个下降为78个,仅占原波长变量数的5.63%。由此表明,GA-CARS联合变量选择方法可以有效筛选脐橙SSC的波长变量,提高预测模型的稳定性和预测精度。     

不同柑橘品种全果制汁适宜性分析

为了研究不同品种柑橘全果制汁的品质特性,对全国7 个地区30 个不同品种柑橘（包括蜜橘、椪柑、冰糖橙、脐橙等）的理化、营养、加工等18 项指标进行测定,并采用主成分分析法筛选出适宜全果制汁的品种。结果表明：不同品种的柑橘全果中籽数和总黄酮、柠檬苦素、诺米林含量差异较大,而出汁率、色度等指标差异较小。经主成分分析提取出6 个特征值大于1的因子,前6 个主成分分别对应柑橘全果苦味因子、加工因子、颜色因子、营养因子、酸度因子和甜度因子,其累积方差贡献率为82.566%。从6 个因子综合考虑,发现‘纽荷尔脐橙-II’、‘福本脐橙-I’、‘福本脐橙-II’、‘冰糖甜橙’、‘道州脐橙’、‘崀丰7904脐橙’为较适宜全果制汁的品种。本研究结果为选择适用于全果制汁的柑橘品种提供了参考依据。     

Spray evaporation of liquid foods

‘Spray evaporation’ based on the principle of adiabatic humidification was examined as an evaporation technique for the concentration of two types of representative liquid foods, namely, fruit juice and milk. The concentration of apple juice could be increased from 10.0 to 13.0 °Brix by this technique without application of heat utilizing the humidity potential to an extent of 89%. Employing heated process air during processing increased the amount of water evaporation owing to increased saturation humidity level, enabling higher concentrations to be achieved in apple juice (48 °Brix), reconstituted milk (29 °Brix) and single toned milk (22 °Brix). Feed flow rate had an inverse effect on the final concentration under otherwise similar conditions. Evaporative cooling associated with spray evaporation actually delivered the concentrate at a relatively lower temperature. By manipulating the operating conditions, humidity potential could be utilized to the extent of 55% with heated process air. The spray evaporation technique seemed to have a good potential for the concentration of liquid foods.