Quantification of Nitrogen Status in Rice by Least Squares Support Vector Machines and Reflectance Spectroscopy

The estimation of nitrogen status non-destructively in rice was performed using canopy spectral reflectance with visible and near-infrared reflectance (Vis/NIR) spectroscopy. The canopy spectral reflectance of rice grown with different levels of nitrogen inputs was determined at several important growth stages. This study was conducted at the experiment farm of Zhejiang University, Hangzhou, China. The soil plant analysis development (SPAD) value was used as a reference data that indirectly reflects nitrogen status in rice. A total of 64 rice samples were used for Vis/NIR spectroscopy at 325–1075 nm using a field spectroradiometer, and chemometrics of partial least square (PLS) was used for regression. The correlation coefficient (r), root mean square error of prediction, and bias in prediction set by PLS were, respectively, 0.8545, 0.7628, and 0.0521 for SPAD value prediction in tillering stage, 0.9082, 0.4452, and −0.0109 in booting stage, and 0.8632, 0.7469, and 0.0324 in heading stage. Least squares support vector machine (LS-SVM) model was compared with PLS and back propagation neural network methods. The results showed that LS-SVM was superior to the conventional linear and non-linear methods in predicting SPAD values of rice. Independent component analysis was executed to select several sensitive wavelengths (SWs) based on loading weights; the optimal LS-SVM model was achieved with SWs of 560, 575–580, 700, 730, and 740 nm for SPAD value prediction in booting stage. It is concluded that Vis/NIR spectroscopy combined with LS-SVM regression method is a promising technique to monitor nitrogen status in rice.     

A comparative study for the quantitative determination of soluble solids content,pH and firmness of pears by Vis/NIR spectroscopy

Visible and near infrared (Vis/NIR) spectroscopy was investigated to determine the soluble solids content (SSC), pH and firmness of different varieties of pears. Two-hundred forty samples (80 for each variety) were selected as sample set. Two-hundred ten pear samples (70 for each variety) were selected randomly for the calibration set, and the remaining 30 samples (10 for each variety) for the validation set. Partial least squares (PLS) and least squares-support vector machine (LS-SVM) with different spectral preprocessing techniques were implemented for calibration models. Different wavelength regions including Vis, NIR and Vis/NIR were compared. It indicated that Vis/NIR (400–1800 nm) was optimal for PLS and LS-SVM models. Then, LS-SVM models were developed with a grid search technique and RBF kernel function. All LS-SVM models outperformed PLS models. Next, effective wavelengths (EWs) were selected according to regression coefficients. The EW-LS-SVM models were developed and a good prediction precision and stability was achieved compared with PLS and LV-LS-SVM models. The correlation coefficient of prediction (r_p), root mean square error of prediction (RMSEP) and bias for the best prediction by EW-LS-SVM were 0.9164, 0.2506 and −0.0476 for SSC, 0.8809, 0.0579 and −0.0025 for pH, whereas 0.8912, 0.6247 and −0.2713 for firmness, respectively. The overall results indicated that the regression coefficient was an effective way for the selection of effective wavelengths. LS-SVM was superior to the conventional linear PLS method in predicting SSC, pH and firmness in pears. Therefore, non-linear models may be a better alternative to monitor internal quality of fruits. And the EW-LS-SVM could be very helpful for development of portable instrument or real-time monitoring of the quality of pears.     

Evaluation of Least Squares Support Vector Machine Regression and other Multivariate Calibrations in Determination of Internal Attributes of Tea Beverages

This research aimed to explore the relationship between internal attributes (pH and soluble solids content) of tea beverages and diffuse reflectance spectra. Three multivariate calibrations including least squares support vector machine regression (LSSVR), partial least squares (PLS), and radial basis function (RBF) neural network were adopted for development of internal attributes determination models. Ten kinds of tea beverages including green tea and black tea were selected for visible and near infrared reflectance (Vis/NIR) spectroscopy measurement from 325 to 1,075 nm. As regard the kernel function, least squares–support vector machine regression models were built with both linear and RBF kernel functions. Grid research and tenfold cross-validation procedures were adopted for optimization of LSSVR parameters. The generalization ability of LSSVR models were evaluated by adjusting the number of samples in the training set and testing set, and sensitive wavelengths that were closely correlated with the internal attributes were explored by analyzing the regression coefficients from linear LSSVR model. Excellent LSSVR models were built with r = 0.998, standard error of prediction (SEP) = 0.111, for pH and r = 0.997, SEP = 0.256, for soluble solids content, and it can be found that the LSSVR models outperformed the PLS and RBF neural network models with higher accuracy and lower error. Six individual sensitive wavelengths for pH were obtained, and the corresponding pH determination model was developed with r = 0.994, SEP = 0.173, based on these six wavelengths. The soluble solids content determination model was also developed with r = 0.977, SEP = 0.173, based on seven individual sensitive wavelengths. The above results proved that Vis/NIR spectroscopy could be used to measure the pH and soluble solids content in tea beverages nondestructively, and LSSVR was an effective arithmetic for multivariate calibration regression and sensitive wavelengths selection.     

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.     

Detection of Organic Acids and pH of Fruit Vinegars Using Near-Infrared Spectroscopy and Multivariate Calibration

Near-infrared (NIR) spectroscopy was investigated to determine the acetic, tartaric, formic acids and pH of fruit vinegars. Optimal partial least squares (PLS) models were developed with different preprocessing. Simultaneously, the performance of least squares-support vector machine (LS-SVM) models was compared with three kinds of inputs, including wavelet transform (WT), latent variables, and effective wavelengths (EWs). The results indicated that all LS-SVM models outperformed PLS models. The optimal correlation coefficient (r), root mean square error of prediction and bias for validation set were 0.9997, 0.3534, and −0.0110 for acetic acid by WT-LS-SVM; 0.9985, 0.1906, and 0.0025 for tartaric acid by WT-LS-SVM; 0.9987, 0.1734, and 0.0012 for formic acid by EW-LS-SVM; and 0.9996, 0.0842, and 0.0012 for pH by WT-LS-SVM, respectively. The results indicated that NIR spectroscopy (7,800–4,000 cm⁻¹) combined with LS-SVM could be utilized as a precision method for the determination of organic acids and pH of fruit vinegars.     

Applying Near-Infrared Spectroscopy and Chemometrics to Determine Total Amino Acids in Herbicide-Stressed Oilseed Rape Leaves

Near-infrared (NIR) spectroscopy was investigated to determine the total amino acids (TAA) in oilseed rape (Brassica napus L.) leaves under a new herbicide—propyl 4-(2-(4,6-dimethoxypyrimidin-2-yloxy)benzylamino)benzoate (ZJ0273)—stress. In full-spectrum partial least squares (PLS) models, direct orthogonal signal correction (DOSC) was the best preprocessing method. Successive projections algorithm (SPA) was used to select the relevant variables. Multiple linear regression (MLR), PLS, and least squares-support vector machine (LS-SVM) were used for calibration. The DOSC–SPA–LS-SVM model achieved the best prediction performance with correlation coefficients r = 0.9968 and root mean squares error of prediction (RMSEP) = 0.2950 comparing all SPA–MLR, SPA–PLS, and SPA–LS-SVM models. Some parsimonious direct functions were also developed based on the DOSC–SPA wavelength (1,340 nm) such as linear, index, logarithmic, binominal, and exponential functions. The best performance was achieved by direct exponential function with r = 0.9968 and RMSEP = 0.2943. The overall results indicated that NIR was able to determine the TAA in herbicide-stressed oilseed rape leaves, and the DOSC–SPA was quite helpful for the development of detection sensors and the monitoring of the growing status and herbicide effect on field crop oilseed rape.     

Measurement of Soluble Solids Content and pH of Yogurt Using Visible/Near Infrared Spectroscopy and Chemometrics

Visible/near infrared spectroscopy (Vis/NIRs) technique was applied to non-destructive quantification of sugar and pH value in yogurt. Partial least squares (PLS) analysis and least squares support vector machine (LS-SVM) were implemented for calibration models. In this paper, three brands (Mengniu, Junyao, and Guangming) were set as the calibration, and the remaining two brands (Yili and Shuangfeng) were used as prediction set. In the LS-SVM model, the correlation coefficient (r), root mean square error of prediction, and bias in prediction set were 0.9427, 0.2621°Brix, 1.804e−09 for soluble solids content, and 0.9208, 0.0327, and 1.094e−09 for pH, respectively. The correlation spectra corresponding to the soluble solids content and pH value of yogurt were also analyzed through PLS method. LS-SVM model was better than PLS models for the measurements of soluble solids content and pH value. The results showed that the Vis/NIRs combined with LS-SVM models could predict the soluble solids content and pH value of yogurt.     

Measurement of Soluble Solid Contents and pH of White Vinegars Using VIS/NIR Spectroscopy and Least Squares Support Vector Machine

Visible and near-infrared (VIS/NIR) spectroscopy combined with least squares support vector machine (LS-SVM) was employed to determine soluble solid contents (SSC) and pH of white vinegars. Three hundred twenty vinegar samples were distributed into a calibration set (240 samples) and a validation set (80 samples). Partial least squares (PLS) analysis was implemented for the regression model and extraction of latent variables (LVs). The selected LVs were used as LS-SVM input variables. Finally, LS-SVM models with radial basis function kernel were achieved with the comparison of PLS models. The results indicated that LS-SVM outperformed PLS models. The correlation coefficient (r), root mean square error of prediction, bias, and residual prediction deviation for the validation set were 0.988, 0.207°Brix, 0.183, and 6.4 for SSC whereas these were 0.988, 0.041, ?0.002, and 6.5 for pH, respectively. The overall results indicated that VIS/NIR spectroscopy and LS-SVM could be used as a rapid alternative method for the prediction of SSC and pH of white vinegars, and the results could be helpful for the fermentation process and quality control monitoring of white vinegar production.     

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.     

Non-destructive measurement of acidity of Chinese bayberry using Vis/NIRS techniques

Chinese bayberry (Myrica rubra Siebold and Zuccarini) is cultivated in southeast China for its edible fruits. In this research, the potential of using the visible/near infrared spectroscopy (Vis/NIRS) was investigated for measuring the acidity of Chinese bayberry, and the relationship was established between non-destructive Vis/NIRS measurement and the acidity of Chinese bayberry. Intact Chinese bayberry fruit was measured by reflectance Vis/NIR in 325–1075 nm range. The data set as the logarithms of the reflectance reciprocal (absorbance (log 1/R)) was analyzed in order to build the best prediction model for this characteristic, using several spectral pretreatments and multivariate calibration techniques such as partial least square regression (PLS). The model for prediction the acidity (r=0.963), standard error of prediction (SEP) 0.21 with a bias of 0.138; showed an excellent prediction performance. The Vis/NIRS technique has significantly greater accuracy for determining the acidity. This non-destructive, fast and accuracy technology can be used in food industry that would be beneficial to human health.     

Rapid Non-destructive Detection of Spoilage of Intact Chicken Breast Muscle Using Near-infrared and Fourier Transform Mid-infrared Spectroscopy and Multivariate Statistics

Near-infrared (NIR) transflectance and Fourier transform-infrared (FT-IR) attenuated total reflectance spectra of intact chicken breast muscle packed under aerobic conditions and stored at 4° for 14 days were collected and investigated for their potential use in rapid non-destructive detection of spoilage. Multiplicative scatter correction-transformed NIR and standard normal variate-transformed FT-IR spectra were analysed using principal component analysis (PCA), partial least-squares discriminant analysis (PLS2-DA) and outer product analysis (OPA). PCA and PLS2-DA regression failed to completely discriminate between days 0 and 4 samples (total viable count (TVC) days 0 and 4 = 5.23 and 6.75 log₁₀ cfu g⁻¹) which had bacterial loads smaller than the accepted levels (8 log₁₀ cfu g⁻¹) of sensory spoilage detection but classified correctly days 8 and 14 samples (TVC days 8 and 14 = 9.61 and 10.37 log₁₀ cfu g⁻¹). OPA performed on both NIR and FT-IR datasets revealed several correlations that highlight the effect of proteolysis in influencing the spectra. These correlations indicate that increase in free amino acids and peptides could be the main factor in the discrimination of intact chicken breast muscle. This investigation suggests that NIR and FT-IR spectroscopy can become useful, rapid, non-destructive tools for spoilage detection.     

Phenolic Content and Antioxidant Activity of Moscatel Dessert Wines from the Setúbal Region in Portugal

Moscatel wines from Setúbal were analyzed for their total phenolic (mean value 1,243 mg gallic acid equivalents/L), and total flavonoid (mean value 248 mg catechin/L) composition by spectrophotometric and chromatographic methods were used to quantify phenolic compounds as benzoic acids, cinnamic acids, stilbens, and some flavonoids. Antioxidant activity of the wines was evaluated by 1,1-diphenyl-2-picrylhydrazyl (DPPH; mean value 70.7% inhibition), ferric reducing antioxidant power (FRAP; mean value 3,098 mg of Trolox equivalents/L) and oxygen radical absorbance capacity (ORAC; mean value 10,724 μmol/L) assays. Results were analyzed using principal component analysis which allowed samples to be grouped in terms of both winemaking producer and vintage. By plotting correlation loadings, it was possible to understand which variables were responsible for sample distribution. The correlation between results obtained for variables show that, in general, total flavonoid content is a better estimation of antioxidant activity in Moscatel samples (r _{ORAC/flavonoids} = 0.832, r _{FRAP/flavonoids} = 0.677) than total phenolic content (r _{ORAC/phenolics} = 0.680, r _{FRAP/phenolics} = 0.372). No major correlations were detected for DPPH assay results (r _{DPPH/flavonoids} = 0.283, r _{DPPH/phenolics} = 0.271). Chromatographic profiles showed important differences among Moscatel wines. Gallic acid contents and results of antioxidant activity tests were strongly correlated (r values in the range 0.74–0.92). Correlations of the results obtained for antioxidant activity tests with contents of other phenolic compounds such as ethyl caffeate, ethyl gallate, caffeic acid, protocatechuic acid, and t-caftaric acid depend on sample and type of test employed. Presented at the “AOAC Europe section international workshop: Enforcement of European Legislation on Food and Water: Analytical and Toxicological Aspects”, in Lisbon, April 2008, and published in abstract form.     

Infrared spectroscopy and chemometrics for the starch and protein prediction in irradiated rice

Infrared spectroscopy was investigated to predict components of starch and protein in rice treated with different irradiation doses based on sensitive wavelengths (SWs). Near infrared and mid-infrared regions were compared to determine which one produces the best prediction of components in rice after irradiation. Partial least-squares (PLS) analysis and least-squares-support vector machine (LS-SVM) were implemented for calibration models. The best PLS models were achieved with NIR region for starch and MIR region for protein. Moreover, independent component analysis (ICA) was executed to select several SWs based on loading weights, and the optimal LS-SVM model was achieved with SWs of 1210–1222, 1315–1330, 1575–1625, 1889–1909 and 2333–2356 nm for starch and SWs of 962–1091, 1232–1298, 1480–1497, 1584–1625 and 2373–2398 cm⁻¹ for protein. It indicated that IR spectroscopy combined with LS-SVM could be applied as a high precision way for the determination of starch and protein in rice after irradiation.     

Prediction of Egg Freshness and Albumen Quality Using Visible/Near Infrared Spectroscopy

Important changes occur in egg during storage leading to loss of quality. Prediction of these changes is critical in order to monitor egg quality and freshness. The aim of this research was to evaluate application of visible (VIS) and near infrared (NIR) spectroscopy as a rapid and non-destructive technique for egg quality assessment. Three hundred and sixty intact white-shelled eggs freshly laid by the same flock of hens fed with a standard feed were obtained. They were put under controlled conditions of temperature and humidity (T = 18 °C and RH = 55%) for 16 days of storage. Forty eggs were analyzed at day 0, 2, 4, 6, 8, 10, 12, 14, and 16. Transmission spectral data was obtained in the range from 350 to 2,500 nm. The non-destructive spectral data was compared to egg sample’s Haugh unit (HU) and albumen pH in terms of quality and to the number of storage days in terms of freshness. A partial least squares predictive model was developed and used to link the destructive assessment methods and the number of storage days with the spectral data. The correlation coefficient between the measured and predicted values of HU, albumen pH, and number of storage days were up to 0.94, R ² was up to 0.90 and the root mean square error values for the validation were 5.05, 0.06, and 1.65, respectively. These results showed that VIS/NIR transmission spectroscopy is a good tool for assessment of egg freshness and albumen pH and can be used as a non-destructive method for the prediction of HU, albumen pH, and number of storage days. In addition, the relevant information about these parameters was in the VIS and NIR ranging from 411 to 1,729 nm.     

Improvement of Near-Infrared Spectral Calibration Models for Brix Prediction in ‘Gannan’ Navel Oranges by a Portable Near-Infrared Device

A portable near-infrared (NIR) device was developed to nondestructively predict Brix value in intact ‘Gannan’ navel oranges. This research focused on developing calibration models which were less disturbed by the challenges of portable applications. The spectra of 150 samples were collected in the wavelength range of 820–950 nm. Wavelet transformed (WT) was applied to compress the raw data for improving the optimization efficiency. Classical linear partial least squares regression and nonlinear least squares support vector regression (LSSVR) were applied to building calibration models. By comparison, both prediction precision and optimization efficiency of the compressed regression models were improved. The LSSVR models outperformed the PLS models with higher accuracy and lower error. LSSVR combined with WT compression (WT–LSSVR) produced the best correlation coefficient value (r) and the root mean squared error of prediction of 0.918 and 0.321 ^oBrix. Based on these results, WT–LSSVR is to be a promising method to improve precision and optimization efficiency of NIR spectral calibration models for Brix prediction in ‘Gannan’ navel oranges by the portable near-infrared device.     

Application of Electronic Nose and Statistical Analysis to Predict Quality Indices of Peach

In this study, responses of a sensor array were employed to establish a quality index model able to describe the different picking date of peaches. The principal component regression (PCR) and partial least-squares regressions (PLS) model represent very good ability in describing the quality indices of the selected three sets of peaches in calibration and prediction. The results showed that the PLS model represents a good ability in predicting quality index, with high correlation coefficients (R = 0.86 for penetrating force [CF]; R = 0.83 for sugar content [SC]; R = 0.83 for pH) and relatively low standard error of prediction (SEP; 8.77 N, 0.299 °Brix, and 0.2 for CF, SC, and pH, respectively). The PCR model had high correlation coefficients (R = 0.84, 0.82, 0.78 for CF, SC, and pH, respectively) between predicted and measured values and a relatively low SEP (7.33 N, 0.44 °Brix, 0.21 for CF, SC, and pH, respectively) for prediction. These results prove that the electronic noses have the potential to assess fruit quality indices.     

FTIR Spectroscopy and Direct Orthogonal Signal Correction Preprocessing Applied to Selected Phenolic Compounds in Red Wines

The composition of phenolic compounds plays an important role in food science and nutrition; thus, there is a need of a new method of analysis that is able to speed up the monitoring of product quality parameters. In this view, the amount of selected color components of 145 commercial red wines (total wine color, polymeric pigments, total anthocyanins, and copigmentation index) was investigated using Fourier transform mid-infrared spectroscopy (MIR) combined with partial least squares (PLS) regression. The feasibility of several preprocessing algorithms (first and second derivative, standard normal variate, and direct orthogonal signal correction) was compared in terms of coefficient of determination (R ²) and root mean square error of prediction using an independent test set of wines. The composition of red wines showed great difference in terms of total color (5.07 ± 1.95 AU at 520 nm) compared to copigmentation index (0.66 ± 0.58 AU at 520 nm). The best prediction model was obtained using direct orthogonal signal correction (DOSC) preprocessing. In particular, the prediction of total wine color, total anthocyanins, and polymeric pigments showed a good fitting (R ² ≥ 0.82), whereas copigmentation index was more difficult to be predicted by FTIR (R ² = 0.57). This preliminary study showed the potential of MIR spectroscopy with DOSC–PLS algorithm to successfully analyze selected color components of red wine on a large number of samples in short time with almost no sample preparation and no chemical waste is created.     

On-line prediction of pH values in fresh pork using visible/near-infrared spectroscopy with wavelet de-noising and variable selection methods

The visible/near-infrared (Vis/NIR) reflectance spectroscopy as an on-line approach to assess the pH value in fresh pork was investigated. Multivariate calibration was carried out by using chemometrics. Discrete wavelet transform was applied to de-noise the spectra scanned on-line, and several variable selection methods were proposed to simplify the calibration models. The study found that the model based on the spectra de-noised by Daubechies 6 wavelet (db6) at decomposition level 6, soft thresholding strategy and minimaxi threshold estimator gave reasonable performance (r > 0.900, root mean square error of calibration (RMSEC) = 0.100, cross validation (RMSECV) = 0.139 and prediction (RMSEP) = 0.125). Then, only 15% variables from this model were selected via the method of uninformative variable elimination to develop a simpler model, of which the performance deterioration could be ignored. The results showed that Vis/NIR can be used to predict pH value in fresh pork on-line, and variable selection can provide a simpler, more cost-effective calibration model.     

Analysis of Pork Adulteration in Commercial Burgers Targeting Porcine-Specific Mitochondrial Cytochrome B Gene by TaqMan Probe Real-Time Polymerase Chain Reaction

A TaqMan probe real-time polymerase chain reaction assay was developed for the determination of pork adulteration in commercial burgers. The assay combined porcine-specific primers and TaqMan probe for the selective amplification and detection of a 109-bp fragment of swine cytochrome b (cytb) gene. Specificity test with 10 ng DNA of 11 different meat-providing animal and fish species yielded a quantification cycle (Cq) of 15.5 ± 0.20 for the pork and negative results for the others in a 40-cycle reaction with a change of analysts and sources. Analysis of beef burger formulations with spiked pork showed the assay can determine 100–0.01% contaminated pork with a PCR efficiency (E) of 93.8% and a correlation coefficient (R ²) of 0.991. A plot of actual value against real-time PCR-predicted value also yielded a good linear regression, R ² 0.998, and small root mean square error of calibration, RMSEC 0.42. A strong correlation was found between the partial least square (PLS)-predicted values and real-time PCR-determined values. The accuracy of the method was ≥90% in all determinations of the standard set. Residual analysis also revealed a high precision in all determinations. Finally, a random analysis of 10 ng DNA of commercial burgers from pork, beef, chicken, mutton, and chevon yielded a Cq of 15.56 ± 0.22 to 16.24 ± 0.35 from pork burgers, and negative results from the others, showing the suitability of the assay to determine pork in commercial burgers with a high accuracy and precision.     

Application of Long-Wave Near Infrared Hyperspectral Imaging for Measurement of Soluble Solid Content (SSC) in Pear

Soluble solid content (SSC) in fruit is one of the most crucial internal quality factors, which could provide valuable information for commercial decision-making. Near-infrared (NIR) technique has effective potentials for determining the SSC since NIR was sensitive to the concentrations of organic materials. In this study, a novel NIR technique, long-wave near infrared (LWNIR) hyperspectral imaging with a spectral range of 930–2548 nm, was investigated for measuring the SSC in pear, which has never been examined in the past. A new combination of Monte Carlo-uninformative variable elimination (MC-UVE) and successive projections algorithm (SPA) was proposed to select most effective variables from LWNIR hyperspectral data. The selected variables were used as the inputs of partial least square (PLS) to build calibration models for determining the SSC of ‘Ya’ pear. The results indicated that calibration model built using MC-UVE-SPA-PLS on 18 effective variables achieved the optimal performance for prediction of SSC comparing with other developed PLS models (MC-UVE-PLS and SPA-PLS) by comprehensively considering the accuracy, robustness, and complexity of models. The correlation coefficients between the predicted and actual SSC were 0.88 and 0.88 and the root mean square errors were 0.49 and 0.35 °Brix for calibration and prediction set, respectively. The overall results indicated that long-wave near infrared hyperspectral imaging incorporated to MC-UVE-SPA-PLS model could be applied as an alternative, fast, accurate, and nondestructive method for the determination of SSC in pear.