Wavelength Selection Algorithm Research Articles

Hyperspectral Data for Early Identification and Classification of Potassium Deficiency in Soybean Plants (Glycine max (L.) Merrill)

Identifying potassium (K+) deficiency in plants has traditionally been a difficult and expensive process. Traditional methods involve inspecting leaves for symptoms and conducting a laboratory analysis. These methods are not only time-consuming but also use toxic reagents. Additionally, the analysis is performed during the reproductive stage of growth, which does not allow enough time for corrective fertilization. Moreover, soybean growers do not have other tools to analyze the nutrition status during the earlier stages of development. Thus, this study proposes a quick approach for monitoring K+ in soybean crops using hyperspectral data through principal component analysis (PCA) and linear discriminant analysis (LDA) with a wavelength selection algorithm. The experiment was carried out at the Brazilian National Soybean Research Center in the 2017–2018, 2018–2019, and 2019–2020 soybean crop seasons, at the stages of development V4–V5, R1–R2, R3–R4, and R5.1–R5.3. Three treatments were evaluated that varied in K+ availability: severe potassium deficiency (SPD), moderate potassium deficiency (MPD), and an adequate supply of potassium (ASP). Spectral data were collected using an ASD Fieldspec 3 Jr. hyperspectral sensor. The results showed a variation in the leaf spectral signature based on the K+ availability, with SPD having higher reflectance in the visible region due to a lower concentration of pigments. PCA explained 100% of the variance across all stages and seasons, making it possible to distinguish SPD at an early development stage. LDA showed over 70% and 59% classification accuracies for discriminating a K+ deficiency in the simulation and validation stages. This study demonstrates the potential of the method as a rapid nondestructive and accurate tool for identifying K+ deficiency in soybean leaves.

Pomegranate molasses authentication using hyperspectral imaging system coupled with automatic clustering algorithm.

Pomegranate molasses is made from concentrated pomegranate juice with nothing added. Due to its nutritional value, limitation in production, and high production cost, this product may be adulterated by date syrup. This study was done to differentiate various types of pomegranate molasses and investigate the possibility of nonauthenticity detection in pomegranate molasses samples using the hyperspectral imaging (HSI) technique compared with physicochemical measurement analysis. The physicochemical properties (brix index, sucrose, acidity, ash content, pH, and formalin index) of 24 samples were measured as the reference analysis method, and it was found that the formalin index was a good factor for pomegranate molasses authenticity evaluation. Additionally, an HSI system (400-1000nm) was used as a nondestructive and rapid screening method to capture spectral data of the samples. The evolutionary wavelength selection algorithm was applied to select effective wavelengths in sample clustering based on the obtained Davies-Bouldin index. Next, principal component analysis was used to visually interpret the spectral data of the sample when using the selected wavelengths and the whole spectra of the samples. Finally, an automatic clustering algorithm by the artificial bee colony as an unsupervised method was developed for the clustering of the authentic and nonauthentic samples. The method did not need descriptively labeled samples and obtained agreed satisfactorily with the degree of nonauthenticity in the samples. This study showed that the developed HSI technique coupled with an automatic clustering algorithm could detect date syrup nonauthenticity in pomegranate samples from the level of 5% adulteration.

Simultaneous Determination of Four Catechins in Black Tea via NIR Spectroscopy and Feature Wavelength Selection: A Novel Approach

As a non-destructive, fast, and cost-effective technique, near-infrared (NIR) spectroscopy has been widely used to determine the content of bioactive components in tea. However, due to the similar chemical structures of various catechins in black tea, the NIR spectra of black tea severely overlap in certain bands, causing nonlinear relationships and reducing analytical accuracy. In addition, the number of NIR spectral wavelengths is much larger than that of the modeled samples, and the small-sample learning problem is rather typical. These issues make the use of NIRS to simultaneously determine black tea catechins challenging. To address the above problems, this study innovatively proposed a wavelength selection algorithm based on feature interval combination sensitivity segmentation (FIC-SS). This algorithm extracts wavelengths at both coarse-grained and fine-grained levels, achieving higher accuracy and stability in feature wavelength extraction. On this basis, the study built four simultaneous prediction models for catechins based on extreme learning machines (ELMs), utilizing their powerful nonlinear learning ability and simple model structure to achieve simultaneous and accurate prediction of catechins. The experimental results showed that for the full spectrum, the ELM model has better prediction performance than the partial least squares model for epicatechin (EC), epicatechin gallate (ECG), epigallocatechin (EGC), and epigallocatechin gallate (EGCG). For the feature wavelengths, our proposed FIC-SS-ELM model enjoys higher prediction performance than ELM models based on other wavelength selection algorithms; it can simultaneously and accurately predict the content of EC (Rp2 = 0.91, RMSEP = 0.019), ECG (Rp2 = 0.96, RMSEP = 0.11), EGC (Rp2 = 0.97, RMSEP = 0.15), and EGCG (Rp2 = 0.97, RMSEP = 0.35) in black tea. The results of this study provide a new method for the quantitative determination of the bioactive components of black tea.

Flexible optimization of variables based on exponential and linear attenuation elimination-binary dragonfly algorithm in near infrared spectroscopic analysis

Single Binary Dragonfly Algorithm (Single-BDA) which is an intelligent optimization algorithm, normally needs more calculation time and often obtains unrobust variables. In this study, an Exponential and Linear Attenuation Elimination-Binary Dragonfly Algorithm (ELAE-BDA) is proposed to overcome the problems by combining exponential and linear attenuation functions. The algorithm can quickly eliminate a part of the variables with large RMSECV in the iterative process through the exponential attenuation function first, and use the linear attenuation function at the end of the iteration for meticulous elimination of invalid variables. It avoids the important variables being deleted unexpectedly, greatly improves the running speed of the algorithm, reduces the randomness of results, and significantly improves the analysis ability of the model. Three near-infrared spectral datasets are used as research objects to evaluate the performance of the ELAE-BDA algorithm, and 6 wavelength selection algorithms including Single-BDA, GA, CARS, MC-UVE, SCARS, and fiPLS are used for comparison. The results show that the ELAE-BDA algorithm can obtain results with almost the smallest number of wavelengths and the highest stability. The established PLSR models have the best prediction with the lowest RMSECV and RMSEP, indicating that the proposed algorithm can provide a more accurate and effective results of wavelength optimization for near-infrared spectral analysis.

Non-destructive online detection of early moldy core apples based on Vis/NIR transmission spectroscopy

Apple moldy core is a fungus-infested disease that is extremely insidious, usually occurring inside the fruit, making it very difficult to distinguish from the exterior with the naked eye. Using VIS/NIR transmission spectroscopy, this study successfully detected moldy core apples. By combining four wavelength selection algorithms (CARS, CARS-SPA, MC-UVE, and MC-UVE-SPA) with four classifiers (SVM, ELM, KNN, and LDA-KNN), discrimination models were established for two-class and three-class classifications. MC-UVE-SPA-LDA-KNN achieved an AUC of 0.99 and an accuracy of 98.82% for two-class classification, while MC-UVE-SPA achieved an AUC of 0.99 and an accuracy of 97.64% for three-class classification. This confirms MC-UVE-SPA as an effective tool for selecting wavelengths specific to moldy core apples, facilitating precise identification and differentiation of apple states. This study advances dynamic online detection of early-stage moldy core conditions in apples, reducing post-harvest disease occurrence and preserving fruit quality effectively.Graphical

Predictive modeling of milk adulteration with urea content using the gray wolf optimization algorithm and long and short-term memory network model

Traditional methods for the determining of urea contaminants in milk, including chromatography, spectrophotometry, and electrochemical processes, have drawbacks such as long testing times and sample destruction. However, hyperspectral imaging technology provides a fast, easy-to-operate, and real-time alternative. In this study, five preprocessing methods are employed, including standard scalar, standard normal variational, first derivative, multivariate scattering correction algorithm, and Savitzky-Golay smoothing. To further enhance the accuracy of predicting urea content in milk, a prediction optimization method based on the gray wolf optimization algorithm for the long short-term memory network was proposed, using a competitive adaptive reweighted sampling algorithm feature wavelength selection algorithm. In the optimized model in the test set experiments, the decision coefficient was improved by 0.56% to 0.9906, and root mean square error was reduced by 7.69% to 0.4996 compared to the long short-term memory network model. This study not only provides a theoretical basis but also presents a fast and nondestructive detection method for accurately predicting urea content in milk.

Detection of peach soluble solids based on near-infrared spectroscopy with High Order Spatial Interaction network.

Due to the scalability of deep learning technology, researchers have applied it to the non-destructive testing of peach internal quality. In addition, the soluble solids content (SSC) is an important internal quality indicator that determines the quality of peaches. Peaches with high SSC have a sweeter taste and better texture, making them popular in the market. Therefore, SSC is an important indicator for measuring peach internal quality and making harvesting decisions. This article presents the High Order Spatial Interaction Network (HOSINet), which combines the Position Attention Module (PAM) and Channel Attention Module (CAM). Additionally, a feature wavelength selection algorithm similar to the Group-based Clustering Subspace Representation (GCSR-C) is used to establish the Position and Channel Attention Module-High Order Spatial Interaction (PC-HOSI) model for peach SSC prediction. The accuracy of this model is compared with traditional machine learning and traditional deep learning models. Finally, the permutation algorithm is combined with deep learning models to visually evaluate the importance of feature wavelengths. Increasing the order of the PC-HOSI model enhances its ability to learn spatial correlations in the dataset, thus improving its predictive performance. The optimal model, PC-HOSI model, performed well with an order of 3 (PC-HOSI-3), with a root mean square error of 0.421 °Brix and a coefficient of determination of 0.864. Compared with traditional machine learning and deep learning algorithms, the coefficient of determination for the prediction set was improved by 0.07 and 0.39, respectively. The permutation algorithm also provided interpretability analysis for the predictions of the deep learning model, offering insights into the importance of spectral bands. These results contribute to the accurate prediction of SSC in peaches and support research on interpretability of neural network models for prediction. © 2024 Society of Chemical Industry.

Comparison of feature selection and data fusion of Fourier transform infrared and Raman spectroscopy for identifying watercolor ink.

The identification of different kinds of watercolor inks is an important work in the field of forensic science. Four different kinds of watercolor ink Spectroscopy data fusion strategies (Fourier Transform Infrared spectroscopy and Raman spectroscopy) combined with a non-linear classification model (Extreme Learning Machine) were used to identify the brand of watercolor inks. The study chose Competitive Adaptive Reweighted Sampling (CARS), Random Frog (RF), Variable Combination Population Analysis-Genetic Algorithm (VCPA-GA), and Variable Combination Population Analysis-Iteratively Retains Informative Variables (VCPA-IRIV) to extract characteristic variables for mid-level data fusion. The Cuckoo Search (CS) algorithm is used to optimize the extreme learning machine classification model. The results showed that the classification capacity of the mid-level fusion spectra model was more satisfactory than that of single Infrared spectroscopy or Raman spectroscopy. The CS-ELM models based on infrared spectroscopy used to recognize the watercolor ink according to brands (ZHENCAI, DELI, CHENGUANG, and STAEDTLER) obtained an accuracy of 66.67% in the test set using all spectral datasets. The accuracy of CS-ELM models based on Raman spectroscopy was 67.39%. The characteristic wavelength selection algorithms effectively improved the accuracy of the CS-ELM models. The classification accuracy of the mid-level spectroscopy fusion model combined with the VCPA-IRIV algorithm was 100%. The data fusion method increased effectively spectral information. The method could satisfactorily identify different brands of watercolor inks and support the preservation of artifacts, paintings, and forensic document examination.

Effective correction of dissolved organic carbon interference in nitrate detection using ultraviolet spectroscopy combined with the equivalent concentration offset method.

Nitrate contamination in water sources poses a substantial environmental and health risk. However, accurate detection of nitrate in water, particularly in the presence of dissolved organic carbon (DOC) interference, remains a significant analytical challenge. This study investigates a novel approach for the reliable detection of nitrate in water samples with varying levels of DOC interference based on the equivalent concentration offset method. The characteristic wavelengths of DOC were determined based on the first-order derivatives, and a nitrate concentration prediction model based on partial least squares (PLS) was established using the absorption spectra of nitrate solutions. Subsequently, the absorption spectra of the nitrate solutions were subtracted from that of the nitrate-DOC mixed solutions to obtain the difference spectra. These difference spectra were introduced into the nitrate prediction model to calculate the equivalent concentration offset values caused by DOC. Finally, a DOC interference correction model was established based on a binary linear regression between the absorbances at the DOC characteristic wavelengths and the DOC-induced equivalent concentration offset values of nitrate. Additionally, a modeling wavelength selection algorithm based on a sliding window was proposed to ensure the accuracy of the nitrate concentration prediction model and the equivalent concentration offset model. The experimental results demonstrated that by correcting the DOC-induced offsets, the relative error of nitrate prediction was reduced from 94.44% to 3.36%, and the root mean square error of prediction was reduced from 1.6108 mg L-1 to 0.1037 mg L-1, which is a significant correction effect. The proposed method applied to predict nitrate concentrations in samples from two different water sources shows a certain degree of comparability with the standard method. It proves that this method can effectively correct the deviations in nitrate measurements caused by DOC and improve the accuracy of nitrate measurement.

Maturity Classification of Rapeseed Using Hyperspectral Image Combined with Machine Learning.

Oilseed rape is an important oilseed crop planted worldwide. Maturity classification plays a crucial role in enhancing yield and expediting breeding research. Conventional methods of maturity classification are laborious and destructive in nature. In this study, a nondestructive classification model was established on the basis of hyperspectral imaging combined with machine learning algorithms. Initially, hyperspectral images were captured for 3 distinct ripeness stages of rapeseed, and raw spectral data were extracted from the hyperspectral images. The raw spectral data underwent preprocessing using 5 pretreatment methods, namely, Savitzky-Golay, first derivative, second derivative (D2nd), standard normal variate, and detrend, as well as various combinations of these methods. Subsequently, the feature wavelengths were extracted from the processed spectra using competitive adaptive reweighted sampling, successive projection algorithm (SPA), iterative spatial shrinkage of interval variables (IVISSA), and their combination algorithms, respectively. The classification models were constructed using the following algorithms: extreme learning machine, k-nearest neighbor, random forest, partial least-squares discriminant analysis, and support vector machine (SVM) algorithms, applied separately to the full wavelength and the feature wavelengths. A comparative analysis was conducted to evaluate the performance of diverse preprocessing methods, feature wavelength selection algorithms, and classification models, and the results showed that the model based on preprocessing-feature wavelength selection-machine learning could effectively predict the maturity of rapeseed. The D2nd-IVISSA-SPA-SVM model exhibited the highest modeling performance, attaining an accuracy rate of 97.86%. The findings suggest that rapeseed maturity can be rapidly and nondestructively ascertained through hyperspectral imaging.

Rapid detection of ash content in black tea using a homemade miniature near-infrared spectroscopy

Ash is a testing index with both health inspection value and quality decision value, and it is an essential detection item in the import and export trade of tea. To realize the rapid and effective quantitative analysis of ash content in tea, this study proposed the use of a homemade miniature near-infrared (NIR) spectroscopy combined with multivariate analysis for the rapid detection of ash content in black tea. First, NIR data of black tea samples from different countries were acquired and optimized by the spectral preprocessing method. Then, the optimized pre-processed spectral data were used as features, and four feature wavelength selection algorithms, such as competitive adaptive reweighted sampling, iteratively retaining informative variables (IRIV), variable combination population analysis (VCPA)-IRIV, and interval variable iterative space shrinkage approach (IVISSA), were utilized to optimize the feature spectra. Finally, the support vector machine regression (SVR) algorithm was employed to construct the quantitative models of ash content in black tea by combining the optimal wavelengths obtained from the four feature selection methods mentioned above. The experimental results showed that the IVISSA-SVR model had the best performance, with correlation coefficient (Rp), root mean square errors of prediction (RMSEP), and relative prediction deviation (RPD) of 0.9546, 0.0192, and 5.59 for the prediction set, respectively. The results demonstrate that a miniature NIR sensing system combined with chemometrics as an effective analytical tool can realize the rapid detection of ash content in black tea.

Rapid determination of two illegal additives in wheat flour by near-infrared spectroscopy and different key wavelength selection algorithms

Rapid and accurate testing of wheat flour for illegal additives is important for maintaining food safety. In this study, a combination of NIR spectroscopy and partial least squares regression (PLSR) was used to predict the levels of two illegal additives (talc powder and benzoyl peroxide) in wheat flour. Different critical wavelength selection algorithms (competitive adaptive reweighted sampling, CARS; random frog, RF; Monte Carlo uninformative variable elimination, MCUVE; CARS-RF; CARS-MCUVE) were used to filter key wavelengths. In our study, R2P for talc and benzoyl peroxide in the full-wavelength PLSR model was 0.995 and 0.964, respectively. The CARS-RF and RF were screened for 10 effective wavelengths. In wheat flour containing talc, the R2P and RPD of the model using the CARS-RF were 0.995 and 14.796, respectively. In wheat flour containing benzoyl peroxide, the R2P and RPD of the model using the RF were 0.923 and 3.697, respectively. CARS-RF and RF algorithms had great potential to accurately predict illegal additives.

Non-destructive prediction of isoflavone and starch by hyperspectral imaging and deep learning in Puerariae Thomsonii Radix

Accurate assessment of isoflavone and starch content in Puerariae Thomsonii Radix (PTR) is crucial for ensuring its quality. However, conventional measurement methods often suffer from time-consuming and labor-intensive procedures. In this study, we propose an innovative and efficient approach that harnesses hyperspectral imaging (HSI) technology and deep learning (DL) to predict the content of isoflavones (puerarin, puerarin apioside, daidzin, daidzein) and starch in PTR. Specifically, we develop a one-dimensional convolutional neural network (1DCNN) model and compare its predictive performance with traditional methods, including partial least squares regression (PLSR), support vector regression (SVR), and CatBoost. To optimize the prediction process, we employ various spectral preprocessing techniques and wavelength selection algorithms. Experimental results unequivocally demonstrate the superior performance of the DL model, achieving exceptional performance with mean coefficient of determination (R2) values surpassing 0.9 for all components. This research underscores the potential of integrating HSI technology with DL methods, thereby establishing the feasibility of HSI as an efficient and non-destructive tool for predicting the content of isoflavones and starch in PTR. Moreover, this methodology holds great promise for enhancing efficiency in quality control within the food industry.

Determination of watermelon soluble solids content based on visible/near infrared spectroscopy with convolutional neural network

The soluble solids content（SSC）of fruits is an essential indicator of fruit taste and flavor. The near-infrared spectroscopy (NIRS) is widely used in fruit SSC detection. A common method to deal with such predictive modelling is the partial least-squares (PLSR). Wavelength selection algorithms are often employed to eliminate redundant wavelengths before constructing the model. However, the wavelength selection algorithms can not only increase the model complexity but also may lose valuable information. In this work, 1D-CNN was used to improve the prediction model of the watermelon SSC. First, the best parameter combination for 1D-CNN was found. The root mean square error of prediction (RMSEP) was 0.21 and the determination coefficient of prediction (Rp2) was 0.97. Then, the performance of PLSR and backpropagation neural network (BPNN) are compared. The results show that the Rp2 of 1D-CNN is improved by 14.1% and 6.6%, respectively. Finally, a gradient-based method for visualizing features of CNN regression models, namely regression activation mapping (RAM), was proposed. The results show that the features of 1D-CNN are consistent with the feature peaks of the spectrum, while the features of the BPNN and PLSR are not obvious.

Determination of residual levels of procymidone in rapeseed oil using near-infrared spectroscopy combined with multivariate analysis

The issue of pesticide residues has always been a hot topic at home and abroad. A method for the quantitative detection of procymidone residues in grain and oil products using near-infrared (NIR) spectroscopy has been proposed. First, a NIR spectrometer was used to collect spectral data from rapeseed oil samples with different concentrations of procymidone residues. Based on full-spectrum data, the wavelength points selected by bootstrapping soft shrinkage (BOSS) algorithm, competitive adaptive reweighted sampling (CARS) algorithm, and variable combination population analysis (VCPA) algorithm then were compared and were quantified using support vector regression (SVR) model. Simultaneously, the prediction results of the SVR model optimized by dung beetle optimizer (DBO) algorithm and pigeon-inspired optimization (PIO) algorithm were compared using the full-spectrum data. Finally, the wavelength selection algorithms and parameter optimization algorithms with the best prediction results were selected for comparison and combination. In light of the outcomes, the three spectral characteristic wavelength selection algorithms and the two optimization algorithms can improve the coefficient of determination (RP2) and reduce the root mean square error of prediction (RMSEP). The SVR model that utilizing CARS and PIO algorithm demonstrates the best generalization performance among all models evaluated, and the RP2 is 0.9939 with a RMSEP of 2.3435 mg·kg−1. The results indicate that the high-precision and rapid detection of procymidone in edible oil can be achieved using the SVR model optimized by input feature and parameter based on NIR spectral data. This has great significance in ensuring the safety of grain and oil food.

Detection of early bruises on apples using hyperspectral reflectance imaging coupled with optimal wavelengths selection and improved watershed segmentation algorithm.

Bruises caused by mechanical collision during the harvesting and storage and transportation period are difficult to detect using traditional machine vision technologies because there is no obvious difference in appearance between bruised and sound tissues. As a result of its fast and non-destructive characteristics, hyperspectral imaging technology is a potential tool for non-destructive detection of fruit surface defects. In the present study, visible near infrared hyperspectral reflectance images of healthy apples and bruised apples at 6, 12 and 24 h were obtained. To reduce hyperspectral data dimension, optimal wavelength selection algorithms including principal component analysis (PCA) and band ratio methods were utilized to select the effective wavelengths and enhance the contrast between bruised and sound tissues. Then pseudo-color image transformation technology combining with improved watershed segmentation algorithm (IWSA) were employed to recognize the bruise spots. The result obtained showed that band ratio images obtained better detection performance than that of PCA. The G component derived from pseudo-color image of followed by IWSA obtained the best segmentation performance for bruise spots. Finally, a multispectral imaging system for the detection of bruised apple was developed to verify the effectiveness of the proposed two-band ratio algorithm, obtaining recognition rates of 93.3%, 92.2% and 92.5% for healthy, bruised and overall apples, respectively. The bruise detection algorithm proposed in the present study has potential to detect bruised apple in online practical applications and hyperspectral reflectance imaging offers a useful reference for the detection of surficial defects of fruit. © 2023 Society of Chemical Industry.

Non-destructive pre-symptomatic detection of gray mold infection in kiwifruit using hyperspectral data and chemometrics

Application of hyperspectral imaging (HSI) and data analysis algorithms was investigated for early and non-destructive detection of Botrytis cinerea infection. Hyperspectral images were collected from laboratory-based contaminated and non-contaminated fruits at different day intervals. The spectral wavelengths of 450 nm to 900 nm were pretreated by applying moving window smoothing (MWS), standard normal variates (SNV), multiplicative scatter correction (MSC), Savitzky–Golay 1st derivative, and Savitzky–Golay 2nd derivative algorithms. In addition, three different wavelength selection algorithms, namely; competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), and successive projection algorithm (SPA), were executed on the spectra to invoke the most informative wavelengths. The linear discriminant analysis (LDA), developed with SNV-filtered spectral data, was the most accurate classifier to differentiate the contaminated and non-contaminated kiwifruits with accuracies of 96.67% and 96.00% in the cross-validation and evaluation stages, respectively. The system was able to detect infected samples before the appearance of disease symptoms. Results also showed that the gray-mold infection significantly influenced the kiwifruits’ firmness, soluble solid content (SSC), and titratable acidity (TA) attributes. Moreover, the Savitzky–Golay 1st derivative-CARS-PLSR model obtained the highest prediction rate for kiwifruit firmness, SSC, and TA with the determination coefficient (R2) values of 0.9879, 0.9644, 0.9797, respectively, in calibration stage. The corresponding cross-validation R2 values were equal to 0.9722, 0.9317, 0.9500 for firmness, SSC, and TA, respectively. HSI and chemometric analysis demonstrated a high potential for rapid and non-destructive assessments of fungal-infected kiwifruits during storage.

Determination of corn protein content using near-infrared spectroscopy combined with A-CARS-PLS.

In order to quickly and accurately determine the protein content of corn, a new characteristic wavelength selection algorithm called anchor competitive adaptive reweighted sampling (A-CARS) was proposed in this paper. This method first lets Monte Carlo synergy interval PLS (MC-siPLS) to select the sub-intervals where the characteristic variables exist and then uses CARS to screen the variables further. A-CARS-PLS was compared with 6 methods, including 3 feature variable selection methods (GA-PLS, random frog PLS, and CARS-PLS) and 2 interval partial least squares methods (siPLS and MWPLS). The results showed that A-CARS-PLS was significantly better than other methods with the results: RMSECV=0.0336, R2 c=0.9951 in the calibration set; RMSEP=0.0688, R2 p=0.9820 in the prediction set. Furthermore, A-CARS reduced the original 700-dimensional variable to 23 variables. The results showed that A-CARS-PLS was better than some wavelength selection methods, and it has great application potential in the non-destructive detection of protein content in corn.

Establishment of a multi-position general model for evaluation of watercore and soluble solid content in ‘Fuji’ apples using on-line full-transmittance visible and near infrared spectroscopy

Watercore and soluble solid content (SSC) are important indicators for assessment of the internal quality of apples. Visible and near infrared spectroscopy (Vis-NIRS) has been successfully applied for on-line analysis of internal quality of apples. In practical application, the position of the detected apples is usually uncertain, which limits the effective application of traditional model developed based on the fixed position or the optimal position. This study established a multi-position general model for evaluation of watercore and SSC in ‘Fuji’ apples using on-line Vis-NIRS. First, the full-transmittance spectra of three positions (P1–P3) of each apple were acquired, and subsequently, LS-SVM and PLS-DA models were constructed based on full wavelength data of single and multiple positions for watercore classification, and LS-SVM and PLS models were also constructed based on the same data for the quantitative prediction of SSC. Then, the detection ability of the constructed single position and the multi-position general models for three independent position samples was compared to determine the optimal full wavelength models; Finally, different wavelength selection algorithms including MC-UVE, BOSS, SPA and their combinations were employed to optimize the full wavelength models. Results showed that the multi-position general MC-UVE-SPA-PLS-DA model was the optimal for classification of watercore apples with average accuracy of 95.96 %, and the multi-position general MC-UV-BOSS-LS-SVM model was the optimal for quantitative detection of SSC with RP of 0.856 and RMSEP of 0.723 %. The overall study revealed that the multi-position general model, combined with full-transmittance Vis-NIRS, was a better choice for on-line detection of watercore and SSC in ‘Fuji’ apples.

A New Method for Spectral Wavelength Selection Based on Multiple Linear Regression Combined with Ant Colony Optimization and Genetic Algorithm

Wavelength selection is one of the key steps in quantitative spectral analysis, which reduces the computation time while also improving the prediction accuracy of the model. In this paper, we propose a wavelength selection algorithm based on the ant colony optimization (ACO), in which the absolute value of the regression coefficient of the multiple linear regression (MLR) model is used as the basis for evaluating the importance of wavelengths, and the absolute value of the regression coefficient after full wavelength MLR modeling is used as the initial pheromone value of the ant colony optimization (MLR-ACO). In each iteration, the absolute value of the regression coefficient corresponding to each wavelength of the individual with the highest fitness value is used as the basis for a pheromone update. The crossover operator is introduced in MLR-ACO (MLR-ACO-GA), and the individuals with the top 100 fitness values in MLR-ACO are used as the initial population of the genetic algorithm (GA). A selected frequency of wavelengths greater than the threshold among MLR-ACO individuals is calculated. A number of coarse interval points are generated according to the selected frequency, and a coarse crossover operation is performed at the coarse interval points. Fine crossover points are randomly generated within the coarse interval, and fine crossover operations are performed within the coarse interval to exploit the potential of combining excellent individuals in MLR-ACO with each other as much as possible. MLR-ACO can well solve the problem of traditional ACO initial pheromone scarcity, and MLR-ACO-GA can avoid MLR-ACO falling into a local optimum to a certain extent and be more flexible in the selection of the number of wavelengths, which can give full play to the advantages of MLR-ACO.