Accurate and nondestructive detection of apple brix and acidity based on visible and near-infrared spectroscopy

Analyzing the Quality Parameters of Apples by Spectroscopy from Vis/NIR to NIR Region: A Comprehensive Review

Spectroscopic methods deliver a valuable non-destructive analytical tool that provides simultaneous qualitative and quantitative characterization of various samples. Apples belong to the world's most consumed crops and with the current challenges of climate change and human impacts on the environment, maintaining high-quality apple production has become critical. This review comprehensively analyzes the application of spectroscopy in near-infrared (NIR) and visible (Vis) regions, which not only show particular potential in evaluating the quality parameters of apples but also in optimizing their production and supply routines. This includes the assessment of the external and internal characteristics such as color, size, shape, surface defects, soluble solids content (SSC), total titratable acidity (TA), firmness, starch pattern index (SPI), total dry matter concentration (DM), and nutritional value. The review also summarizes various techniques and approaches used in Vis/NIR studies of apples, such as authenticity, origin, identification, adulteration, and quality control. Optical sensors and associated methods offer a wide suite of solutions readily addressing the main needs of the industry in practical routines as well, e.g., efficient sorting and grading of apples based on sweetness and other quality parameters, facilitating quality control throughout the production and supply chain. This review also evaluates ongoing development trends in the application of handheld and portable instruments operating in the Vis/NIR and NIR spectral regions for apple quality control. The use of these technologies can enhance apple crop quality, maintain competitiveness, and meet the demands of consumers, making them a crucial topic in the apple industry. The focal point of this review is placed on the literature published in the last five years, with the exceptions of seminal works that have played a critical role in shaping the field or representative studies that highlight the progress made in specific areas.

Rapid determination of the main components of corn based on near-infrared spectroscopy and a BiPLS-PCA-ELM model

To evaluate corn quality quickly, the feasibility of near-infrared spectroscopy (NIRS) coupled with chemometrics was analyzed to detect the moisture, oil, protein, and starch content in corn. A backward interval partial least squares (BiPLS)-principal component analysis (PCA)-extreme learning machine (ELM) quantitative analysis model was constructed based on BiPLS in conjunction with PCA and the ELM. The selection of characteristic spectral intervals was accomplished by BiPLS. The best principal components were determined by the prediction residual error sum of squares of Monte Carlo cross validation. In addition, a genetic simulated annealing algorithm was utilized to optimize the parameters of the ELM regression model. The established regression models for moisture, oil, protein, and starch can meet the demand for corn component detection with the prediction determination coefficients of 0.996, 0.990, 0.974, and 0.976; the prediction root means square errors of 0.018, 0.016, 0.067, and 0.109; and the residual prediction deviations of 15.704, 9.741, 6.330, and 6.236, respectively. The results show that the NIRS rapid detection model has higher robustness and accuracy based on the selection of characteristic spectral intervals in conjunction with spectral data dimensionality reduction and nonlinear modeling and can be used as an alternative strategy to detect multiple components in corn rapidly.

The knowledge domain and emerging trends in apple detection based on NIRS: A scientometric analysis with CiteSpace (1989-2021)

In this paper, 317 literature in the Web of Science (WoS) related to research on apple by near-infrared spectroscopy (NIRS) were drawn on the knowledge map of the number of literature, the co-occurrence network of authors and institutions, the co-occurrence and clustering of keywords based on CiteSpace. And a related analysis was carried out. Combined with the results of visual analysis and related literature, the research hotspots were sorted out and discussed. This paper provides a certain reference for relevant researchers to study in this field and provides a new method for macroscopically grasping the current status of apple quality detection research, which helps new researchers to quickly integrate into this field and obtain more valuable scientific information.

Nondestructive detection of anthocyanin content in fresh leaves of purple maize using hyperspectral data

Anthocyanins are widely used in the food industry as an additive, improving antioxidant capacity and strengthening the human immune system. However, rapid and nondestructive detection methods are lacking. This study aimed to develop a rapid and nondestructive method to detect anthocyanin content in fresh purple maize leaves using hyperspectral reflectance. Sensitivity bands were screened by analyzing the correlation between the spectrum and anthocyanin, chlorophyll, and moisture content in maize leaves with models constructed. Through a combination of the sensitivity bands of the three components, the interference of chlorophyll and moisture on the spectral detection of anthocyanin in fresh leaves was analyzed. The results showed that the anthocyanin sensitivity band was approximately 550 nm. The determination coefficient and root mean square error of the optimal hyperspectral model were 0.766 and 4.215 mg/g, respectively. After excluding chlorophyll and moisture interference, the anthocyanin content detection accuracy was improved by only 2% compared to that of the original. These results indicate that hyperspectral technology can be used to nondestructively detect anthocyanin content in fresh purple maize leaves with good accuracy. Chlorophyll and moisture in the leaves did not significantly influence anthocyanin content.

Nondestructive determination of SSC in an apple by using a portable near-infrared spectroscopy system

The soluble solids content (SSC) is an important factor in the internal quality detection of apples. It is essential to have reliable and high-speed measurement of the SSC. However, almost all traditional equipment is inconvenient and expensive. We designed a handheld nondestructive SSC detector based on near-infrared (NIR) spectroscopy, which is composed of a portable NIR spectrometer, cloud server, smartphone app, and prediction model of SSC. We preprocessed the spectrum with multiplicative scatter correction (MSC), standard normal variable transformation (SNV), and Savitzky-Golay (S-G) smoothing algorithms. Besides, the linear weight reduction of the particle swarm optimization algorithm is carried out, and we establish the model of an extreme learning machine optimized with the improved particle swarm optimization (IPSO-ELM) algorithm. The R², root mean square error of prediction (RMSEP), and residual prediction deviation (RPD) of the model are 0.993, 0.0155, and 11.6, respectively, which are better than the traditional model obviously. In addition, the number of wavelengths reduced from 228 to 70 as the model is simplified with the uninformative variable elimination (UVE) method. The time of training is reduced by 29.30% compared with the original spectrum. It can be verified that the IPSO-ELM model has good prediction performance, and the NIR diffuse reflectance spectroscopy is a reliable nondestructive measurement of SSC in apples.

Nondestructive nitrogen content estimation in tomato plant leaves by Vis-NIR hyperspectral imaging and regression data models

The present study aims to estimate nitrogen (N) content in tomato (Solanum lycopersicum L.) plant leaves using optimal hyperspectral imaging data by means of computational intelligence [artificial neural networks and the differential evolution algorithm (ANN-DE), partial least squares regression (PLSR), and convolutional neural network (CNN) regression] to detect potential plant stress to nutrients at early stages. First, pots containing control and treated tomato plants were prepared; three treatments (categories or classes) consisted in the application of an overdose of 30%, 60%, and 90% nitrogen fertilizer, called N-30%, N-60%, N-90%, respectively. Tomato plant leaves were then randomly picked up before and after the application of nitrogen excess and imaged. Leaf images were captured by a hyperspectral camera, and nitrogen content was measured by laboratory ordinary destructive methods. Two approaches were studied: either using all the spectral data in the visible (Vis) and near infrared (NIR) spectral bands, or selecting only the three most effective wavelengths by an optimization algorithm. Regression coefficients (R) were 0.864±0.027 for ANN-DE, 0.837±0.027 for PLSR, and 0.875±0.026 for CNN in the first approach, over the test set. The second approach used different models for each treatment, achieving R values for all the regression methods above 0.96; however, it needs a previous classification stage of the samples in one of the three nitrogen excess classes under consideration.

Nondestructive estimation of three apple fruit properties at various ripening levels with optimal Vis-NIR spectral wavelength regression data

Nondestructive estimation of fruit properties during their ripening stages ensures the best value for producers and vendors. Among common quality measurement methods, spectroscopy is popular and enables physicochemical properties to be nondestructively estimated. The current study aims to nondestructively predict tissue firmness (kgf/cm), acidity (pH level) and starch content index (%) in apples (Malus M. pumila) samples (Fuji var.) at various ripening stages using visible/near infrared (Vis-NIR) spectral data in 400–1000 nm wavelength range. Results show that non-linear regression done by an artificial neural network-cultural algorithm (ANN-CA) was able to properly estimate the investigated fruit properties. Moreover, the performance of the proposed method was evaluated for Vis-NIR data based on optimal NIR wavelength values selected by a genetic optimization tool. Regression coefficients (R) in estimated acidity, tissue firmness, and starch content properties were R=0.930±0.014, R=0.851±0.014, and R=0.974±0.006, respectively, using only the three most effective wavelengths from the acquired spectra.

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Automatic regression infrared imaging system for nondestructive estimation of physicochemical properties in apple fruit.

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Tissue firmness (kgf/cm), acidity (pH) and starch content (%) fruit properties estimated.

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Hardware used includes: spectrophotometer, photo-detector, light source, and optical fiber.

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System comprises both variable wavelength Vis-NIR ranges and fixed optimal NIR wavelength windows.

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Results: regression plots, regression (R) and determination (R²) coefficient boxplots, measured vs estimated value graphs.

Nondestructive detection model of soluble solids content of an apple using visible/near-infrared spectroscopy combined with CARS and MPGA

Selecting the decisive characteristic variables is particularly important to analyze the soluble solids content (SSC) of an apple with visible/near-infrared spectroscopy (VIS-NIRS) technology. The multi-population genetic algorithm (MPGA) was applied to variable selection for the first time, to the best of our knowledge. A hybrid variable selection method combined competitive adaptive reweighted sampling (CARS) with MPGA (CARS-MPGA) was proposed. In this method, CARS was firstly used to shrink the variable space, and then the MPGA was used to further fine select the characteristic variables. Based on CARS-MPGA, a nondestructive quantitative detection SSC model of an apple was established and compared with the models established by different variable selection methods, such as successive projections algorithm, synergy interval partial least squares, and genetic algorithm. The experiments showed that the CARS-MPGA model was the best. The number of modeling variables was only 64, and the determination coefficients, root mean squared error, and residual predictive deviation for the prediction set were 0.853, 0.443, and 2.612, respectively. The results demonstrated that the CARS-MPGA is a reliable variable selection method and can be used for fast nondestructive detection SSC of an apple.