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.     

Nondestructive measurement of firmness and soluble solids content for apple fruit using hyperspectral scattering images

Nondestructive sensing is critical to assuring postharvest quality of apple fruit and consumer acceptance and satisfaction. The objective of this research was to use a hyperspectral scattering technique to acquire spectral scattering images from apple fruit and develop a data analysis method relating hyperspectral scattering characteristics to fruit firmness and soluble solids content (SSC). Hyperspectral scattering images were obtained from ‘Golden Delicious’ (GD) and red ‘Delicious’ (RD) apples, which were generated by a broadband beam over the spectral region between 500 nm and 1,000 nm. Mean and standard deviation spectra were extracted from the hyperspectral scattering images. A hybrid method combining the backpropagation feedforward neural network with principal component analysis was used to develop prediction models for fruit firmness and SSC. The neural network models were able to predict fruit firmness with r ² = 0.76 and the standard error of prediction (SEP) of 6.2 N for GD, and r ² = 0.55 and SEP = 6.1 N for RD. Better SSC predictions were obtained with r ² = 0.79 and 0.64 and SEP = 0.72% and 0.81% for GD and RD, respectively. Hyperspectral scattering is promising for assessing internal quality, especially the firmness, of apples. Mention of commercial products is only to provide factual information for the reader and does not imply endorsement of the US Department of Agriculture.     

Visible/near-infrared hyperspectral imaging prediction of textural firmness of grass carp (Ctenopharyngodon idella) as affected by frozen storage

Textural firmness is a primary determinant of consumer acceptance for evaluating freshness quality of fish fillet flesh. The objective of this study was to investigate the potential of using visible and near-infrared hyperspectral imaging (400–1000 nm) for non-destructive prediction of firmness quality of grass carp fillet as affected by frozen storage. Fillet samples were frozen at − 20 °C for 24 h and then stored at 4 °C for thawing over five days. Hyperspectral images were obtained at different thawing stages and their corresponding spectral data were extracted. Two calibration models were established between the extracted spectral data and the reference firmness values measured by the traditional mechanical method by using partial least squares regression (PLSR) and least-square support vector machine (LS-SVM) analysis. Three approaches of regression coefficients (RC) from PLSR analysis, genetic algorithm (GA) and successive projection algorithm (SPA) were utilized to recognize the most important wavelengths that possessed the greatest influence and sensitivity on the firmness prediction based upon the whole spectral range. By comparing the above-mentioned three variable selection methods, seven optimal wavelengths (450, 530, 550, 616, 720, 955 and 980 nm) were selected by GA and its corresponding simplified prediction model of GA-LS-SVM was also obtained, showing the best performance with the highest determination coefficient (R²_P) of 0.941 and the lowest root mean square error estimated by prediction (RMSEP) of 1.229. The overall results of this study suggested that hyperspectral imaging technique has a potential for fast and non-destructive prediction and analysis of textural firmness of grass carp fillets as affected by frozen storage.     

FT-NIR spectroscopy for the quality characterization of apricots (Prunus armeniaca L.)

Abstract: The nondestructive assessment of apricot fruit quality (Bora cultivar) was carried out by means of FT-NIR reflectance spectroscopy in the wavenumber range 12000 to 4000 cm⁻¹. Samples were harvested at four different ripening stages and scanned by a fiber optical probe immediately after harvesting and after a storage of 3 d (2 d at 4 °C and 1 d at 18 °C); the flesh firmness (FF), the soluble solids content (SSC), the acidity (A), and the titratable acidity (malic and citric acids) were then measured by destructive methods. Soft independent modeling of class analogy (SIMCA) analysis was used to classify spectra according to the ripening stage and the storage: partial least squares regression (PLS) models to predict FF, SSC, A, and the titratable acidity were also set-up for both just harvested and stored apricots. Spectral pretreatments and wavenumber selections were conducted on the basis of explorative principal component analysis (PCA). Apricot spectra were correctly classified in the right class with a mean classification rate of 87% (range: 80% to 100%). Test set validations of PLS models showed R² values up to 0.620, 0.863, 0.842, and 0.369 for FF, SSC, A, and the titratable acidity, respectively. The best models were obtained for the SSC and A and are suitable for rough screening; a lower power prediction emerged for the other maturity indices and the relative predictive models are not recommended. Practical Application : The results of the study could be used as a tool for the assessment of the ripening stage during the harvest and the quality during the postharvest storage of apricot fruits.     

Maturity sorting index of dragon fruit: Hylocereus polyrhizus

The purpose of this research was to investigate maturity prediction of red flesh dragon fruit based on non-destructive measures. Specific weight, sphericity, color value L, a, b and light reflectance spectrum were linearly combined by partial least squares regression (PLSR) analysis. The PLSR models could predict days after fruit set, weight ratio and total soluble solids relatively well with standard deviation divided by standard error of prediction (RPD) of 2.86, 2.45 and 2.38, respectively. Date after fruit set, total soluble solids, total acid, ratio of total soluble solids and total acidity and weight ratio were transformed into a principal component 1 (PC1) by the principal component analysis and used to represent a single maturity index. The PLSR model with non-destructive parameters resulted in an improved performance in the prediction of the maturity index (PC1) with a RPD increase to 3.49. The model could be further simplified but retained a comparable accuracy by the application of a log (R680/R550) in place of the light reflectance spectrum.     

Maturity effects on dielectric properties of apples from 10 to 4500 MHz

Dielectric properties of external surface, internal tissue and juice of Fuji apples during the last two months of tree-ripening were measured with an open-ended coaxial-line probe and a network analyzer at 24 °C from 10 to 4500 MHz. The firmness, soluble solids content (SSC), pH, moisture content and electrical conductivity were also measured to determine whether permittivity is related to apple quality. During the tree-ripening period, the permittivity and electrical conductivity did not reveal obvious trends. Moisture content and SSC remained essentially constant, while the firmness decreased and pH increased with maturity. No obvious correlations were found between permittivity and firmness, moisture content or pH. The linear relationship between surface permittivity and SSC was poor (R² < 0.2). The best R² for linear regression between loss tangent of tissue and SSC and between dielectric constant of juice and SSC was 0.61 and 0.67, respectively, at 4500 MHz. Further studies are needed to assess the potential usefulness of dielectric properties for sensing apple maturity or internal quality.     

Prediction of firmness and soluble solids content of blueberries using hyperspectral reflectance imaging

Currently, blueberries are inspected and sorted by color, size and/or firmness (or softness) in packing houses, using different inspection techniques like machine vision and mechanical vibration or impact. A new inspection technique is needed for effectively assessing both external features and internal quality attributes of individual blueberries. This paper reports on the use of hyperspectral imaging technique for predicting the firmness and soluble solids content (SSC) of blueberries. A pushbroom hyperspectral imaging system was used to acquire hyperspectral reflectance images from 302 blueberries in two fruit orientations (i.e., stem and calyx ends) for the spectral region of 500–1000 nm. Mean spectra were extracted from the regions of interest for the hyperspectral images of each blueberry. Prediction models were developed based on partial least squares method using cross validation and were externally tested with 25% of the samples. Better firmness predictions (R = 0.87) were obtained, compared to SSC predictions (R = 0.79). Fruit orientation had no or insignificant effect on the firmness and SSC predictions. Further analysis showed that blueberries could be sorted into two classes of firmness. This research has demonstrated the feasibility of implementing hyperspectral imaging technique for sorting blueberries for firmness and possibly SSC to enhance the product quality and marketability.     

Quality evaluation of pickling cucumbers using hyperspectral reflectance and transmittance imaging—Part II. Performance of a prototype

This paper reports on the development and evaluation of methods and algorithms for detecting both external and internal quality of pickling cucumbers using the hyperspectral reflectance and transmittance images acquired by an online prototype described in a previous paper [1]. Experiments were performed in 2 years on ‘Journey’ pickling cucumbers, some of which were subjected to mechanical stress to induce internal defect in seed cavity. Hyperspectral images of the ‘Journey’ pickling cucumbers were collected under reflectance, transmittance, and their combination modes. Partial least squares analysis was performed on spectra extracted from the hyperspectral images to predict firmness, color, and the presence of internal defect. The system performed well on predicting skin color (chroma and hue) with the coefficient of determination (R ²) ranging between 0.75 and 0.77; however, it had poor prediction of fruit firmness. Transmittance data in the spectral region of 675–1,000 nm provided the best detection of internal defect for the test pickling cucumbers, with the detection accuracy up to 99%. Up to the best four wavelength combinations were identified using linear discriminant analysis for internal defect detection. The hyperspectral imaging technique can be used for simultaneous detection of color, size, and internal defect on pickling cucumbers.     

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 measurement of soluble solid content of navel orange fruit by visible-NIR spectrometric technique with PLSR and PCA-BPNN

A relationship was established between the soluble solid content (SSC) of navel orange fruit determined by destructive measurement and visible-near infrared diffuse reflectance spectra in the wavelength range of 350-1800 nm. Multiplicative scatter correction (MSC) and standard normal variate correction (SNV) were applied to the spectra, partial least squares regression (PLSR) and back propagation neural network (BPNN) based on principal component analysis (PCA) were used to develop the models for predicting the SSC of intact navel orange fruit. Thirty-eight unknown samples were used to evaluate the performance of these models. The principal component analysis-back propagation (PCA-BPNN) method with MSC spectral pretreatment obtain the best predictive results, resulting in correlation coefficient, root mean square error of prediction (RMSEP), average difference between predicted and measured values (Bias) of 0.90, 0.68 °Brix and 0.16 °Brix, respectively. Experimental results indicate that PCA-BPNN is a suitable tool to model the non-linear complex system, with additional advantages over PLSR, and the vis/NIR spectrometric technique can be used for measuring the SSC of intact navel orange fruit, nondestructively.     

Chemometrics coupled with ultraviolet spectroscopy: a tool for the analysis of variety,adulteration, quality and ageing of apple juices

This study demonstrates the use of UV spectroscopy (UV) in combination with chemometrics as a simple and feasible approach for analysis of variety, adulteration, quality and ageing of apple juice. The results show that PCA‐UV is adequate to differentiate apple juice varieties and adulteration. The percentage of the adulterant can be detected by PLSR‐UV with RMSE < 0.7783% and R² > 0.9980. For the evaluation of juice quality, PLSR‐UV (RMSE = 0.2555–2.3448; R² = 0.7276–0.9816) is recommended for the prediction of soluble solids, ascorbic acid, total flavonoids, total sugar and reducing sugar, whilst PCR‐UV (RMSE = 0.0000–2.7426; R² = 0.7073–1.0000) is adequate for the prediction of pH and antioxidant activity. In addition, PLSR‐UV may be used to predict the storage time with RMSE = 0.4681 day and R² = 0.9832. Therefore, UV coupled with chemometrics has potential to be developed as a portable tool for the detection of variety, adulteration, quality and ageing of not only apple juices, but also other fruit and vegetable juices.     

Non-destructive measurement of firmness and soluble solids content in bell pepper using NIR spectroscopy

The potential of near infrared (NIR) reflectance spectroscopy over the range 780–1690 nm was investigated to measure the soluble solids content (SSC) and firmness of bell pepper fruit. Partial least squares (PLS) calibration models were constructed based on a calibration dataset which included data from two cultivars (Solution and Ferrari) and two harvest times (2005 and 2006). The effect of Savitzky–Golay second derivative preprocessing and extended multiplicative signal correction (EMSC) on the accuracy of the calibration models was investigated and the best results were obtained with the former. The SECV were equal to 5.9 N and 0.59 °Brix for firmness and SSC, respectively. When the model was applied to an external data set including data from cv. Solution and a different harvest season, the satisfactory SEP values of 4.49 N and 0.7 °Brix were obtained, but for firmness a bias of 5.6 N was observed. From these results it can be concluded that NIR spectroscopy can be used as a non-destructive technique for measuring the SSC in bell pepper, but that further research is needed to make it robust for firmness prediction.     

WAVELENGTH SELECTION FOR PREDICTING PHYSICOCHEMICAL PROPERTIES OF APPLE FRUIT BASED ON NEAR-INFRARED SPECTROSCOPY

ABSTRACT

Instrumental evaluation tools for fruit quality monitoring are important in the production and postharvest processes as well as in marketing. In the present study, near‐infrared spectroscopy (600–1,100 nm) was applied to study the correlation with fruit soluble solid content (SSC ), fruit flesh firmness and water content of apples (cv. “Fuji”). Genetic algorithm and correlation coefficient (r) method were used to select the most sensitive wavelength combinations, and partial least squares regression analysis was applied to calibrate fruit quality parameter. The validation of models based on the most sensitive wavelengths gave good predictions with an r value of 0.94 and a standard error of cross validation (SECV) of 0.85°Brix for SSC; r = 0.89 and SECV = 7.54 N/cm² for firmness; and r = 0.96 and SECV = 0.92% for water content. The reduced data set of sensitive wavelengths were found feasible for predicting internal fruit quality.

PRACTICAL APPLICATIONS

Soluble solid content, firmness and water content are important quality attributes of apples. A nondestructive measurement technique will be valuable for monitoring and sorting apple fruit so that high quality, uniform fresh products can be delivered to the marketplace. In the present study, fruit analyses using the entire near‐infrared fruit spectra or a reduced data set of sensitive wavelengths were compared. The results demonstrate that the selected combinations of sensitive wavelengths were feasible for measuring apple quality properties. The recent research findings provide researchers and instrumentation engineers with information on the performance of different methods to select appropriate wavelengths for reducing the amount of data, e.g., in developing portable or online sensing systems.

     

STUDY ON GENETIC ALGORITHMS-BASED NIR WAVELENGTH SELECTION FOR DETERMINATION OF SOLUBLE SOLIDS CONTENT IN FUJI APPLES

ABSTRACT

The determination of soluble solids content (SSC) in intact apples was analyzed by means of near‐infrared spectroscopy using an acousto‐optic tunable filter technology. Genetic algorithms (GAs) were performed to select wavelengths within the range from 1,065 to 1,625 nm. Partial least squares regression (PLSR) model based on GAs was compared with the nonvariable selection. Furthermore, the majority of selected wavelengths that brought on that result have been analyzed with regard to the typical absorption bands of sugars and the low‐noise spectral signals. With the GAs approach, the spectral points were reduced from 88 to 17, and the low relative standard deviation of prediction (RSD_p) (6.02%) and high coefficient of calibration (0.914) were achieved. The results showed that PLSR combined with GAs not only simplified and optimized the calibration model, but also improved the prediction effect of the calibration model. It was concluded that the GAs‐based method extracted relevant SSC information from special spectral regions and left out the useless spectral points simultaneously.

PRACTICAL APPLICATIONS

The determination of soluble solids content (SSC) in intact apples is quite important for assessing the internal quality of fruit. However, the portable spectrophotometer did not provide a reliable spectral signal. The multivariate modeling of the entire spectra not only computes complexly, but also increases spectral interferences. In the present study, an acousto‐optic tunable filter spectrometer was used for analysis. Genetic algorithms (GAs) were presented for the wavelength selection in order to predict the SSC in intact apples, based on partial least squares regression of near‐infrared spectral data. The results demonstrated that the wavelength selection based on GAs can build a simple model and can improve the performance of the model. It was also shown that the GAs‐based method extracted relevant information from special spectral regions and left out the useless spectral points simultaneously. It is valuable to establish a nondestructive measurement for developing online sensing systems.

     

基于近红外高光谱成像技术的涩柿SSC含量无损检测

对150个涩柿采集900~1 700nm波段的近红外高光谱图像信息,利用蒙特卡罗—无信息变量消除(MC-UVE)和连续投影算法(SPA)对感兴趣区域光谱进行波长优选。通过MC-UVE-SPA优选出924.69,928.05,1 112.72,1 270.91,1 365.3,1 402.42,1 453.06,1 547.69nm 8个特征波长,对应的光谱反射率作为柿子可溶性固性物含量(SSC)检测的偏最小二乘回归(PLSR)检测模型输入,其预测集相关系数rpre=0.942,预测集均方根误差RMSEP=1.009°Brix。结果表明,MC-UVE-SPA可以有效提取与柿子SSC含量相关的特征信息,从而保留较少的波长建立较好的预测模型。     

基于回归分析法预测湖景蜜露桃果实可溶性固形物含量

目的：建立一种科学预测桃果实可溶性固形物含量的方法。方法：以湖景蜜露水蜜桃为实验材料,利用多元回归、线性回归和二次多项式回归分析研究果皮色差、单果质量、带皮硬度、去皮硬度与可溶性固形物含量的关系。结果：1）以原始基础数据为依据,较难建立稳定的预测可溶性固形物含量的多元回归方程。2）通过将数据进行分组,以红色饱和度（a*）、色调角（h）、红色饱和度/黄色饱和度（a*/b*）不能建立稳定的回归方程;单果质量与可溶性固形物含量具有极显著的线性回归关系,但二者所建立的二次多项式预测可溶性固形物含量效果差;带皮硬度、去皮硬度与可溶性固形物含量的线性回归方程预测性差,而建立的二次多项式R2都较高,回归关系均达极显著水平,方程：可溶性固形物含量=－0.128 2×带皮硬度2＋1.403 5×带皮硬度＋11.418 0和可溶性固形物含量=－0.481 8×去皮硬度2＋1.975 0×去皮硬度＋13.290 0预测湖景蜜露果实可溶性固形物含量效果较好。结论：采用二次多项式回归法研究带皮硬度、去皮硬度与可溶性固形物含量的关系进而进行桃果实成熟度预测是可行的。     

Comparison of NIR and MIR spectroscopic methods for determination of individual sugars,organic acids and carotenoids in passion fruit

In this work, NIR and MIR spectroscopy was investigated and compared for predicting passion fruit ripening parameters as sugars, organic acids and carotenoids. Spectra of 56 samples of the lyophilized passion fruit were collected using an integrating sphere in NIR range and attenuated total reflectance accessory in MIR range. Individual sugars (sucrose, glucose and fructose), organic acids (malic and citric acids) and carotenoids (β-carotene) contents were determined by reference methods. Spectral and reference data were analyzed by principal component analysis. Partial least square regression (PLSR) was used to establish calibration models. MIR technique was better than the NIR technique for glucose (R²_v = 0.942), fructose (R²_v = 0.855), sucrose (R²_v = 0.818), total sugar (R²_v = 0.914) and citric acid (R²_v = 0.913) content determination. On the other hand, NIR was superior for total acids (R²_v = 0.903) content determination. For malic acid and β-carotene contents both methods were unsatisfactory due to low concentrations of these constituents in the passion fruits.     

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.     

Prediction of postharvest dry matter, soluble solids content, firmness and acidity in apples (cv. Elshof) using NMR and NIR spectroscopy: a comparative study

Dry matter (DM), soluble solids content (SSC), firmness and acidity by proton nuclear magnetic resonance (NMR) T₂ relaxometry and near infrared (NIR) spectroscopy were investigated on a total of 390 apples (cv. Elshof). The fruit came from four different pre- or postharvest treatments and covered a large range of DM (11.4–20.0 %) and SSC values (10.5–18.3 °Brix). NIR was superior in predicting DM (R ² = 0.82) and SSC (R ² = 0.80), compared to NMR (R ² = 0.50 and R ² = 0.58). However, NMR relaxometry was able to detect multiple water populations assigned to different water pools in the apples and variation in the water distribution between different pre- and postharvest treatments. Differences in the mobility of the vacuole water (population T₂₄) were consistent with changes in fruit firmness. In conclusion, even though NIR is superior in predicting DM and SSC, NMR provides useful information about the intrinsic water state and its distribution in the fruit.     

基于数字光处理技术的梨可溶性固形物含量的无损速测研究

目的为降低近红外光谱仪器制造成本,将近红外技术推广到农业生产一线,检验自主集成水果品质无损快速分析仪实验样机性能。方法以北京大兴产黄金梨、园黄梨为例,利用基于数字光处理技术内核的实验样机采集数据,采用偏最小二乘回归结合全交互验证算法分别建立黄金梨、园黄梨以及两种梨的可溶性固形物含量定量校正模型,并采用外部验证集对模型预测性能做进一步验证。结果黄金梨、园黄梨以及两种梨的可溶性固形物含量模型的测定系数R~2分别为0.6136、0.6576、0.5105,RMSEC分别为0.71、0.79、0.87:交互验证测定系数R~2_(CV)分别为0.5332、0.5076、0.4193,RMSECV分别为0.78、0.96、0.95;外部验证集相关系数r分别为0.7239、0.6825、0.6550,RMSEP分别为0.83、1.03、0.94。结论基于数字光处理技术内核自主集成的水果品质无损快速分析仪器在梨可溶性固形物含量的无损速测以及降低仪器制造成本方面具有一定的应用潜力。