Apple Soluble Solids Content Research Articles

Convolutional neural networks fusing spectral shape features with attentional mechanisms for accurate prediction of soluble solids content in apples

Convolutional neural networks fusing spectral shape features with attentional mechanisms for accurate prediction of soluble solids content in apples

Dynamic Nondestructive Detection Models of Apple Quality in Critical Harvest Period Based on Near-Infrared Spectroscopy and Intelligent Algorithms.

Apples are usually bagged during the growing process, which can effectively improve the quality. Establishing an in situ nondestructive testing model for in-tree apples is very important for fruit companies in selecting raw apple materials for valuation. Low-maturity apples and high-maturity apples were acquired separately by a handheld tester for the internal quality assessment of apples developed by our group, and the effects of the two maturity levels on the soluble solids content (SSC) detection of apples were compared. Four feature selection algorithms, like ant colony optimization (ACO), were used to reduce the spectral complexity and improve the apple SSC detection accuracy. The comparison showed that the diffuse reflectance spectra of high-maturity apples better reflected the internal SSC information of the apples. The diffuse reflectance spectra of the high-maturity apples combined with the ACO algorithm achieved the best results for SSC prediction, with a prediction correlation coefficient (Rp) of 0.88, a root mean square error of prediction (RMSEP) of 0.5678 °Brix, and a residual prediction deviation (RPD) value of 2.466. Additionally, the fruit maturity was predicted using PLS-LDA based on color data, achieveing accuracies of 99.03% and 99.35% for low- and high-maturity fruits, respectively. These results suggest that in-tree apple in situ detection has great potential to enable improved robustness and accuracy in modeling apple quality.

Coating with phenolic branched-chain fatty acid reduces Listeria innocua populations on apple fruit

An antimicrobial coating was produced by mixing phenolic branched-chain fatty acid (PBC-FA) with glycerol and a carboxymethyl cellulose solution (CMC) at pH 7. The resulting PBC-FA-CMC solution formed an emulsion with an average droplet size of 77 nm. The emulsion in the coating solution was stable for at least 30 days at 20 °C. The in vitro antimicrobial activity of the film formed from the PBC-FA emulsion was tested against a mixture of 3 strains of Listeria innocua (7 log CFU/mL). Film with a concentration of 1000 μg/mL of PBC-FA effectively reduced the population of L. innocua below the limit of detection (<1.48 log CFU/mL) in vitro. The effect of the 1000 μg/mL PBC-FA-CMC coating formulation was then evaluated against L. innocua inoculated on “Gala” apples. Results showed that compared with the non-coated control, the coating reduced L. innocua populations by ~2 log CFU/fruit and ~6 log CFU/fruit on the apple when enumerated on tryptic soy agar and selective media (PALCAM), respectively, indicating that PBC-FA applied as a coating on apples resulted in the sub-lethal injury of bacterial cells. When L. innocua was inoculated onto PBC-FA-coated apples, the L. innocua population decreased by ~4 log CFU/fruit during 14 days of shelf-life at 20 °C. The PBC-FA coating lowered the moisture loss but did not affect the color, firmness, or soluble solids content of apples during the 14-day at 20 °C. Overall, this study revealed that there is a potential that PBC-FA can be used as an antimicrobial coating to inactivate Listeria and preserve the quality of apples.

Research on the upgrade and maintenance method of apple soluble solids content models

AbstractIn this paper, 3 years (2020, 2021, and 2022) of Bingtangxin apples were used to study the upgrade and maintenance method of apple soluble solids content (SSC) model using near infrared (NIR) spectroscopy. Modeling by partial least squares (PLS) and one‐dimensional convolutional neural network (1D‐CNN) algorithm. The “base model” is built with apples in 2020 and then upgrade and maintain it. The upgraded model is used to predict the sample in 2021 and 2022. Three methods were used to upgrade and maintenance the model, which are based on updated data, direct correction‐based method, and similar band‐based method. Among the three upgrade methods, the model maintenance method using updated data had the greatest improvement in prediction correlation coefficients (Rp) in both years, and both obtained good predictions. The use of direct correction resulted in the greatest reduction in the root mean square error of prediction (RMSEP) in both years. However, the Rp of the model is not improved much by using the direct correction method. The model maintenance method using similar bands combined with different modeling methods also gives good prediction results. Finally, after comparison, the model maintenance method of selecting similar bands is preferred. In the case of limited improvement in the effect of using the similar band method, the model is maintained using the method of adding updated data. The investigation of model upgrade and maintenance methods can reduce human and material consumption and make apple SSC models more versatile.Practical applicationsNIR technology is the first choice for online fruit quality inspection, but its inspection process requires constant upgrading and maintenance with models. The quality of apples varies from year to year, and the model built with samples from a single year does not work well in predicting samples from other years, resulting in the need to upgrade and maintain the fruit quality detection model. However, sometimes, although a large amount of experimental material is lost, it still results in a generic calibration model that is too cumbersome and leads to poor prediction results. At this time, finding an effective model upgrade and maintenance method can both reduce the sample consumption and make the model prediction accuracy improve, and also reduce the model complexity and make the maintained model more robust.

Factors Explaining Variations in Soluble Solids Content of Apples during Ripening and Storage

Factors Explaining Variations in Soluble Solids Content of Apples during Ripening and Storage

Prediction of soluble solids content using near-infrared spectra and optical properties of intact apple and pulp applying PLSR and CNN

Soluble solids content (SSC) is one of the most important internal quality attributes of fruit and could be predicted using near-infrared (NIR) spectra and optical properties. Partial least squares regression (PLSR) is a conventional regression method in SSC prediction. In recent years, deep learning methods represented by convolutional neural network (CNN) was suggested to be implied in spectral analysis. However, researchers are inevitably facing problems with regard to the selection of spectral pretreatment methods and the evaluation of the performance of the chosen regression. This study employed PLSR and CNN regression to predict SSC of apple based on the collected diffuse reflectance spectra of intact apple, total reflectance and total transmittance spectra of apple pulp, and the calculated optical property spectra, i.e., absorption coefficient and reduced scattering coefficient spectra of apple pulp. Five different spectral pretreatment methods were exerted on these spectra. Results showed that at a given regression (PLSR or CNN), the built models based on the diffuse reflectance spectra of intact apple had the best SSC prediction, and the built models based on pulp’s reduced scattering coefficient spectra had the poorest prediction performance. The best prediction performance was achieved by PLSR models using Savitzky-Golay with multiple scattering correction (Rp = 0.96, RMSEP = 0.54 %) and CNN regressions using Savitzky-Golay with standard normal variational transformation (Rp = 0.95, RMSEP = 0.59 %), respectively. Additionally, when the unknown original spectra were used for modeling, CNN had a better performance compared to PLSR, indicating the outstanding preponderance of CNN in spectral analysis. This study provides an effective reference for the selection of chemometric method based on NIR spectra.

Detection model transfer of apple soluble solids content based on NIR spectroscopy and deep learning

Transfer and updating of near infrared (NIR) spectroscopy model of fruit internal quality has become the focus of the industrial application. Internet of Things (IoT) and deep learning (DL) were proposed to perform soluble solids content (SSC) model transfer of apple by NIR. A model transfer platform including low-power handheld internal quality terminal and interacting cloud data system had been constructed. An autoencoder (AE) neural network model was developed for the spectral correction and model transfer. The average time for transmitting detection results to the detection terminal was 1.5 to 2.0 s, with a 100% effective transmission rate. After 5000 iterations of training, the correlation coefficient of different detection terminals improved by 55%, and the root mean square error was reduced by 94%. Selected samples from the second batch of apples detected by the No. 1 detection terminal were added to the original neural network for training. After adding 30 samples, the correlation coefficient increased by 13% and the root mean square error decreased by 90%. The results demonstrated that the AE neural network for spectral correction was effective in eliminating differences between devices and significantly reducing the impact of different detection terminals on the accuracy of NIR detection of SSC in apples. Therefore, the NIR detection model transfer technique could be practically exploited for fruit quality control assessment using different detection terminals.

Enhancing model robustness through different optimization methods and 1-D CNN to eliminate the variations in size and detection position for apple SSC determination

The visible/near-infrared (Vis/NIR) spectroscopy technique has been widely used for the online detection of soluble solids content (SSC) in apples. However, external factors such as sample size and detection position can cause spectral distortion, resulting in a decline in detection accuracy. In this study, we aimed to develop a more robust prediction model that can resist the impact of sample size and detection position on the model. Firstly, we collected and analyzed the transmission spectra of apple samples under different sizes and detection positions using a self-designed Vis/NIR spectroscopy online acquisition device. It was found that the effect of fruit size and detection position on Vis/NIR spectra was due to optical path difference. Thus, a diameter correction method was utilized to uniformly correct the obtained absorbance spectra. The performance of local models achieved better results after correction. And then, global models with various preprocessing methods were developed. To further improve the model performance, changeable size moving window (CSMW) and competitive adaptive reweighted sampling (CARS) were utilized to select the effective wavelengths. After that, one dimensional-convolutional neural network (1D-CNN) model was constructed, which outperformed the other models without any preprocessing and optimization methods, and the values of RCal2, RMSEC, RPre2, and RMSEP are 0.953, 0.254 %, 0.900, and 0.371 %, respectively. In this study, conventional PLSR modelling methods and deep 1D-CNN method were compared under the influence of the two external factors, and the result showed that 1D-CNN can serve as a more convenient alternative for apple online SSC determination, which could significantly reduce the complexity of the Vis/NIR spectroscopy modeling process.

Non-destructive determination of soluble solids content in intact apples using a self-made portable NIR diffuse reflectance instrument

The soluble solids content (SSC) is an important internal quality parameter of fruits that is monitored on the fruit market to meet different consumer demands. The aim of this study was to develop a self-made portable near-infrared (NIR) diffuse reflectance instrument to evaluate and monitor the SSC of intact apples. The visible and near-infrared (Vis-NIR) spectra of 118 ‘Fuji’ apples were collected using a Vis-NIR diffuse reflectance spectroscopic measurement system in the spectral range of 450–1100 nm. Savitzky-Golay convolution smoothing and first derivative methods were subsequently applied to eliminate noise interference and baseline shift. Successive projections algorithm (SPA) was performed to extract feature wavelengths from the pretreatment spectra, and the back-propagation artificial neural network (BP-ANN) and multivariate nonlinear regression (MNLR) models were employed to evaluate the predictive ability of the extracted and selected feature wavelengths for the SSC of apples. Seven wavelengths (881, 890, 901, 926, 941, 951, 978 nm) were selected as optimal feature wavelengths from the extracted feature wavelengths (18 wavelengths). The MNLR model (R2 = 0.953, RMSE = 0.391 %) exhibited a high prediction accuracy compared to the BP-ANN model (R2 = 0.865, RMSE = 0.754 %). Based on the results, NIR combined with the MNLR model could be applied in the self-made portable instrument to innovatively monitor the SSC of apples. Finally, a self-made portable NIR diffuse reflectance instrument was designed and developed, and the spectral information of 298 ‘Fuji’ apples was collected using the instrument. According to the results, the MNLR model could well predict the SSC of apples (R2 = 0.871, RMSE = 0.687 %). The overall results also revealed that the developed portable NIR instrument is a promising device for rapid non-destructive monitoring of fruit SSC, thereby meeting the practical application requirement of postharvest commercial processing systems. This instrument could also be beneficial to customers, growers, and producers.

Avaliação não destrutiva de atributos de qualidade em maçãs e peras usando espectroscopia no infravermelho próximo e quimiometria

Abstract The objective of this study was to evaluate the performance of a handheld NIR spectrometer for non-destructive quality analysis of apples and pears produced in the Brazilian Semi-arid region. NIR spectra were acquired with a portable spectrometer in the wavelength range of 750–1065 nm and reference analyses of dry matter content (DMC) and soluble solids content (SSC) were measured weekly during 10 weeks of storage at 0.5 °C. Spectra were pre-processed with standard normal variate and used to develop DMC and SSC models using partial least squares regression with full cross-validation. The models were validated using data not included in the calibration. Satisfactory prediction results were obtained for SSC in apples (R² = 0.58) and pears (R² = 0.55), and for DMC in apples (R² = 0.55) and pears (R² = 0.65). All prediction models showed a relative root mean square error of prediction lower than 8%. These findings indicate that the NIR spectrometer is a promising tool to be used for a rapid and non-destructive determination of internal quality traits in apples and pears.

Achieving robustness to temperature change of a NIR model for apple soluble solids content

AbstractThe temperature difference of fruit itself will affect its near infrared spectrum and the accuracy of its soluble solids content (SSC) prediction model. To eliminate the influence of apple temperature difference on the SSC model, a diffuse transmission dynamic online detection device was used to collect the spectral data of apples at different temperatures, and four methods were used to establish partial least squares correction models: global correction, orthogonal signal processing, generalized least squares weighting and external parameter orthogonal (EPO). The results show that the temperature has a strong influence on the diffuse transmission spectrum of apples. The 20 ºC model can get a satisfactory prediction result when the temperature is constant, and there will be great errors when detecting samples at other temperatures. The effect of temperature must be corrected to establish a more general model. These methods all improve the accuracy of the model, with the EPO method giving the best results; the prediction set correlation coefficient is 0.947, the root mean square error of prediction is 0.489%, and the prediction bias is 0.009%. The research results are of great significance to the practical application of SSC prediction of fruits in sorting workshops or orchards.

Study on the detection of apple soluble solids based on fractal theory and hyperspectral imaging technology

To explore a new method for the detection of soluble solids content (SSC) in apples, the reflectance spectra of apples with different SSC were obtained based on hyperspectral imaging technology in this paper, and fractal measurement of the reflection spectrum curve was carried out based on fractal theory, for using fractal dimension to quantitatively reflect its SSC. The results show that the samples with different SSC have little difference in fractal dimension, and are not sensitive to the change of apple SSC, which cannot show an obvious linear relationship. It can be considered to conduct a study on discrimination with obvious differences between spectral curves, such as variety discrimination and damage discrimination.

An improved method for predicting soluble solids content in apples by heterogeneous transfer learning and near-infrared spectroscopy

Predicting fruit soluble solids content (SSC) is a hot topic in non-destructive detection. Biological variability of fruit decreases the accuracy of the prediction model. Therefore, modeling methods that can reduce the negative effect of biological variability are necessary. In this paper, an improved modeling method based on the partial least squares (PLS) regression was proposed, using convolutional autoencoder and heterogeneous transfer learning for feature extraction. The dataset used for calibration and prediction contains spectra and corresponding SSC values of apples collected from 2012 to 2018. In comparison with the traditional PLS method, the proposed method performed better for long-term SSC prediction of apples with biological variability. The correlation coefficients calculated on validation set were 0.934, 0.940, 0.915, 0.899, 0.901, respectively. The root mean square errors calculated on validation set were 0.736 °Brix, 0.694 °Brix, 0.674 °Brix, 0.571 °Brix, 0.620 °Brix, respectively. Besides, the proposed method could still achieve relatively satisfactory results when the quantity of calibration samples was less.

Calibration of Near Infrared Spectroscopy of Apples with Different Fruit Sizes to Improve Soluble Solids Content Model Performance

The transmission spectrum of apples is affected by the fruit’s size, which leads to poor prediction performance of the soluble solids content (SSC) models built for their different apple sizes. In this paper, three sets of near infrared (NIR) spectra of apples with various apple diameters were collected by applying NIR spectroscopy detection equipment to compare the spectra differences among various apple diameter groups. The NIR spectra of apples were corrected by studying the extinction rates within different apples. The corrected spectra were used to develop a partial least squares prediction model for their soluble solids content. Compared with the prediction model of the soluble solids content of apples without size correction, the Rp of PLSR improved from 0.769 to 0.869 and RMSEP declined from 0.990 to 0.721 in the small fruit diameter group; the Rp of PLSR improved from 0.787 to 0.932 and RMSEP declined from 0.878 to 0.531 in the large fruit diameter group. The proposed apple spectra correction method is effective and can be used to reduce the influence of sample diameter on NIR spectra.

Study on the Effect of Apple Size Difference on Soluble Solids Content Model Based on Near-Infrared (NIR) Spectroscopy

Soluble solids content (SSC) is a vital evaluation index for the internal quality of apples, and NIR spectroscopy is the preferred technique for predicting the SSC of apples. Due to the differences in fruits’ sizes, their SSC prediction models have poor robustness and low prediction accuracy, so it is important to eliminate the effects brought by the differences in fruit sizes to improve the accuracy of fruit sorting models. The NIR spectra of apples with different fruit sizes were collected by applying NIR spectroscopy online detection device, and after various preprocessing of the spectra, the partial least squares (PLS) models of apple SSC were established, respectively, and then the modeling set in the apple fruit size group of 75 mm–85 mm was used to predict the prediction set samples in the apple fruit size group of 65 mm–75 mm and 85 mm–95 mm, respectively. To better address the effects of apple size differences, data fusion techniques were used to perform an intermediate fusion of apple fruit diameter and spectra, firstly, the competitive adaptive reweighting algorithm (CARS) and the continuous projection algorithm (SPA) were used to select spectral variables and build their prediction models for apple SSC, respectively, and the results showed that the models built with 61 spectral variables selected by CARS had better performance, greatly reduced the amount of data involved in modeling, effectively simplified the model, and improved the stability of the model. The apple size variables were added to the wavelength variables selected by CARS, and the data were normalized to establish a PLS model of apple SSC with the normalized spectral and apple fruit diameter data, and the results showed that the size compensation model based on intermediate fusion had the best prediction performance, with the prediction set Rp of 0.886 for fruit diameter of 65 mm–75 mm, RMSEP of 0.536%, and its prediction set Rp was 0.913 and RMSEP was 0.497% for the fruit diameter of 85 mm–95 mm. Therefore, adding the fruit diameter variable to establish the size-compensated model of apple SSC can improve the prediction performance of the model.

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.

Effect of ambient temperature on the model stability of handheld devices for predicting the apple soluble solids content

The soluble solids content (SSC) is an important factor for determining the harvest time and the optimal storage time of apple fruit. However, changes in environmental temperatures cause the apple spectrum to fluctuate, which affects the robustness and accuracy of the model for predicting the apple fruit SSC. A portable detection device applicable for different temperatures was developed using a micro-spectrometer and temperature sensor. The detection time for the device was less than 2 s. The circuit board for collecting and analysing the spectrum signal, the detection light path and the housing of the device were designed, and the optimal integration time (6 ms) and light source power (4.5 W) of the device were determined. A total of 420 apple samples were examined in three ambient temperature ranges (0–2, 10–13, and 19–24 °C). The results showed that the model built independently in a single temperature environment can make a good prediction of the samples in the same temperature range. The application of d-value curve method reduced the influence of temperature on model accuracy to a certain extent. The d-value curve is a variable selection method independent of the reference value y, which describes the characterisation ability of each wavelength to samples in different sampling environments. These results conform that removing temperature-sensitive wavelengths from the independent variables or adding temperature factors can both improve the prediction ability of the device in different temperature environments. The model established by incorporating the temperature factor produced the most accurate predictions, with a predicted correlation coefficient, root mean square error of prediction, and ratio of standard deviation to RMSEP of 0.871, 0.402%, and 2.038, respectively.

基于可见光和近红外光谱的便携式苹果可溶性固形物含量检测仪的研制

In order to detect the soluble solids content of apples quickly and accurately, a portable apple soluble solids content detector based on USB2000 + micro spectrometer was developed. The instrument can communicate with computer terminal and mobile app through network port, Bluetooth and other ways, which can realize the rapid acquisition of apple spectral information. Firstly, the visible / near-infrared spectrum data and soluble solids content information of 160 apple samples were collected; secondly, the spectral data preprocessing methods were compared, and the results showed that the prediction model of sugar content based on partial least square (PLS) method after average smoothing preprocessing was accurate. The correlation coefficient (RP) and root mean square error (RMSEP) of the prediction model were 0.902 and 0.589 ° Brix, respectively. Finally, on the basis of average smoothing preprocessing, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to optimize the wavelength of spectral data, and PLS model was constructed based on the selected 17 characteristic wavelengths, which can increase the accuracy of soluble solids content prediction model, increase the RP to 0.912, and reduce RMSEP to 0.511 ° Brix. The portable visible / near infrared spectrum soluble solids prediction model based on the instrument and method has high accuracy, and the detector can quickly and accurately measure the soluble solids content of apple.

An optimal zone combination model for on-line nondestructive prediction of soluble solids content of apple based on full-transmittance spectroscopy

High accuracy on-line estimation for fruit internal quality is still a challenge due to varying geometric structures and orientations. In this study, multiple full-transmittance spectra were collected using short-integration-time mode for each apple. The signal-to-noise ratio of each collected spectrum changed with the measurement position of the fruit due to the heterogeneity of internal composition. To explore the distribution character of transmittance spectra across the apple structure to guide the development of an on-line fruit quality determination system, a methodology which we called ‘zone combination modelling’ was proposed for selecting the most effective spectra for SSC prediction. The orientation of stem–calyx axis vertical was selected as the preferred orientation for quality prediction of ‘Fuji’ apple based on the analysis of the variation and quality of full-transmittance spectra. The most ineffective and most effective zone combinations for SSC prediction were determined by investigating the effect of transmittance spectra within different zone combinations on SSC prediction ability. Ten effective wavelengths selected from the most efficient zone combination were used to develop an optimal prediction model. Results showed that the contribution of different spectral measurement zones to SSC prediction capability varied and that in particular, those collected from the apple core zone should be removed when building SSC prediction models. The coefficient of determination and root mean square errors of prediction and validation sets of SSC, respectively, were 0.733 and 0.61%, 0.721 and 0.71% for the optimal model, indicating that zone combinations model was promising for SSC prediction of apple.

Non-destructive evaluation of soluble solids content of apples using a developed portable Vis/NIR device

A portable visible and near-infrared (Vis/NIR) device could evaluate and monitor internal qualities of fruit on-tree, as well as during storage conditions after harvest. A portable Vis/NIR device which consisted of a commercial spectrometer in the spectral range of 400–1000 nm, an interactance fibre optic probe, a novel switch system, and a microcontroller, was developed and its ability for apple soluble solids content (SSC) prediction was evaluated. A switch system was designed for spectra collection, resulting in the acquisition of three spectra for each measurement of apple fruit, namely the white reference, dark reference, and sample spectrum, which can be used to correct the spectrum of apple fruit dynamically. The results showed that the dynamic correction was more promising than the static correction in which the reference spectra were obtained only once. A model for SSC was built using partial least square (PLS), with the coefficient of determination of prediction ( R p 2 ), the root mean square error of prediction (RMSEP), and the ratio of the standard deviation of the reference destructive SSC to the RMSEP (RPD) of 0.777, 0.561%, and 2.114, respectively. The model was then embedded in the custom software to make it possible for the portable device to predict SSC of apple directly, followed by validation using independent sets. The validation results gave R p 2 , RMSEP, and RPD of 0.764, 0.672%, 2.029, respectively for SSC prediction under laboratory conditions, and 0.684, 2.777%, and 0.381, respectively for apples on-tree. The prediction results in the field were improved dramatically using the model built by the field data, with R p 2 , RMSEP and RPD of 0.690, 0.604%, and 1.794, respectively. The overall results showed that the developed device had considerable potential to detect the SSC of apple in practical situations.