Method for evaluating tissue structure and viscoelasticity of puree based on optical characteristics

Abstract

The invention relates to a method for evaluating a puree tissue structure and viscoelasticity based on light absorption and light scattering characteristics. The method comprises the following steps: acquiring light absorption and light scattering signals of apple puree in visible-near-infrared and short-wave near-infrared bands under different heat treatment conditions; after collected optical signals are preprocessed through standard normal transformation and a Savitzky-Golay smoothing method, an evaluation model of the apple puree tissue structure (particle area and particle perimeter) and the viscoelasticity (viscosity and viscoelasticity) is constructed based on a partial least square regression algorithm, and effective prediction of the apple puree tissue structure and the viscoelasticity is achieved only through optical characteristic parameters. According to the method, the tissue structure and viscoelasticity of the puree under different heat treatment parts can be evaluated only by acquiring light absorption and scattering signals of the puree, and technical support is provided for a puree processing enterprise to realize rapid, efficient and stable product texture quality monitoring.

Description

Technical Field

[0001] The invention belongs to the technical field of spectral analysis, and relates to the detection of apple puree quality by using visible-near infrared spectroscopy technology and short-wave infrared technology. Background Art

[0002] Fruit puree is a healthy complementary food for special groups such as infants, young children and the elderly. Viscoelasticity has extremely important practical significance in the processing and manufacturing of fruit puree. It not only affects the processing and filling efficiency, but also directly affects the sensory quality and quality safety of the product. On a macro scale, the main evaluation parameters of the viscoelasticity of fruit puree are apparent viscosity, elastic modulus G′ and viscosity modulus G″; on a micro scale, the particle morphology of pulp tissue (size, volume, surface area, particle size distribution) has a significant effect on viscoelasticity and is the primary determining factor. Although the dynamic shear rheometer can accurately measure the macroscopic viscoelastic coefficient of fruit puree, the equipment is expensive, the test time is long, and it cannot achieve rapid real-time detection, which cannot meet the needs of precise production of high-quality fruit puree. Therefore, real-time, rapid and accurate detection of the viscoelasticity of fruit puree has important scientific research value and application prospects.

[0003] Visible (Vis) and near-infrared (NIR) spectroscopy is an important means of rapid quality identification of agricultural products and processed foods. At present, rapid and efficient quality prediction of the chemical quality characteristics of apple puree (soluble solids, dry matter, titratable acidity) has been successfully achieved based on Vis-NIR spectroscopy. However, the accurate detection of the texture characteristics of puree (organic structure, apparent viscosity, viscoelasticity) has not yet been effectively achieved based on Vis-NIR spectroscopy. The fundamental reason is the lack of in-depth exploration of the detection mechanism for the intrinsic relationship between the internal physical properties of puree and the optical response, resulting in poor prediction accuracy and stability. This is a technical bottleneck for achieving stable and precise processing control of puree texture characteristics. When light enters the puree sample, it mainly undergoes absorption and multiple scattering, among which the light absorption coefficient (μ a ) can reflect the change of chemical composition, while the light scattering coefficient (μ' s ) can characterize tissue morphology and structural characteristics. This project systematically analyzes the variation of light absorption and scattering coefficients of fruit puree viscoelastic coefficients and tissue particle morphological parameters, and develops a new method for rapid and accurate quantitative detection of the optical properties of fruit puree tissue structure and viscoelasticity, which is of great significance to improving the level of precision control of fruit puree quality in my country. Summary of the invention

[0004] The purpose of the present invention is to provide a novel method for rapid detection of apple puree tissue particle structure and viscoelasticity. By using a single integrating sphere system combined with an inverse doubling integration method (IAD), the optical characteristic signals of apple puree under different heat treatment conditions are obtained and the pulp tissue particle structure and viscoelasticity parameters (particle area, particle perimeter, apparent viscosity, elastic modulus, viscous modulus) are collected, and pre-processing is performed by standard normal transformation (SNV) and Savitzky-Golay, and the optical correlation parameter system of the tissue particle structure and viscoelasticity of the apple puree after thermal processing is constructed by partial least squares regression (PLSR) algorithm analysis, so as to realize the detection of the tissue particle structure and viscoelasticity of the apple puree after thermal processing by the optical characteristics of the apple puree.

[0005] The benefits of the present invention are as follows: (1) It clarifies the optical response mechanism of the particle structure and viscoelasticity of apple puree during thermal processing, providing an important theoretical basis for optical detection of puree quality; (2) It develops a method for rapidly predicting the texture quality of apple puree, which simplifies the detection steps of apple puree quality. Compared with traditional determination of puree particle structure and viscoelastic parameters, the present invention saves time, reduces labor, and significantly reduces determination costs; (3) It achieves prediction of puree particle structure and viscoelasticity during thermal processing by only rapidly characterizing the optical properties of apple puree, thus enabling food processing companies to perform rapid, efficient, and stable quality control in the apple puree processing stage. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Figure 1 : Flowchart

[0007] Figure 2 : Schematic diagram of fruit puree processing, spectrum collection and sample set division

[0008] Figure 3 :Appearance of fruit puree under different heat treatment conditions

[0009] Figure 4 :(a) Changes in the particle structure of fruit puree under different heat treatment conditions;(b) Changes in the apparent viscosity curve of fruit puree under different heat treatment conditions;(c) Changes in the elastic modulus curve of fruit puree under different heat treatment conditions;(d) Changes in the viscosity modulus curve of fruit puree under different heat treatment conditions

[0010] Figure 5 :(a) Based on FW-μ s ′ Loading diagram for predicting the particle size of puree tissue; (b) Based on Vis-NIR-μ s ′ Loading diagram for predicting puree viscosity; (c) Loading diagram for predicting puree viscosity based on Vis-NIR-μ s ′ Load diagram for predicting elastic modulus of puree; (d) Load diagram for predicting elastic modulus of puree based on Vis-NIR-μ a Loading diagram for predicting the viscosity modulus of puree (where FW-μs ′ is the reduced scattering coefficient in the full band, Vis-NIR-μ s ′ is the reduced scattering coefficient in the visible-near infrared, Vis-NIR-μ a is the absorption coefficient in the visible-near infrared) DETAILED DESCRIPTION

[0011] A rapid characterization method for evaluating the structure and viscoelasticity of fruit puree based on optical properties, the specific implementation method is as follows:

[0012] 1. Experimental Materials and Methods

[0013] Puree processing: The puree processing was repeated for four batches within three weeks at 1°C and 90% relative humidity. After sorting and cleaning, 100 g of apple pulp was weighed and heated at 100°C for 6, 8, 10, 12, 14, 16, 18, 20, and 22 min, respectively. Subsequently, the puree was processed for 30 seconds at 100°C and 3000 rpm / min. After four repetitions of the nine processing conditions, 36 puree sample groups were obtained and placed in sealed plastic bags. The appearance of the puree samples was as follows: Figure 3 As shown;

[0014] Determination of structural indices: A high-resolution digital camera (EOS 600D, Canon, Japan, resolution: 5344 × 3516 pixels) was used to capture two-dimensional images of the fruit puree (diluted 100 times) uniformly dispersed in the culture dish. Subsequently, after automatic thresholding and binarization processing, the area and perimeter of the fruit puree tissue particles were measured using Image J software to determine the tissue particle size.

[0015] Viscoelastic parameters determination: At 22.5 °C, the viscoelasticity of each puree sample was measured using a dynamic shear rheometer equipped with PP50 parallel plates (PhysicMCR301, Anton Paar, Austria), including apparent viscosity curve test and amplitude sweep test. Apparent viscosity test: The measurement gap was set to a fixed value of 1 mm, and then the viscosity was measured at 10-250 s. -1 The viscosity of the puree is measured as a function of shear rate within the shear rate range. The shear rate is recorded as 50s -1 The apparent viscosity value η 50 ; Amplitude sweep test: The fruit puree was subjected to an amplitude sweep test at an angular frequency of 10 rad / s and a strain range of 0.01%-100% to obtain the linear viscoelastic range of the puree, and then the average values ​​of the elastic modulus G′ and the viscous modulus G″ within the linear viscoelastic range were calculated as the elastic modulus G′ and the viscous modulus G″ of the fruit puree.

[0016] Significance and correlation analysis: First, the mean and standard deviation of the same sample group were calculated to express the trend and dispersion of the data; then, IBM SPSS 20 (The SPSS, USA) was used to analyze the tissue particle area, tissue particle perimeter, η 50 , G′ and G″ were subjected to one-way ANOVA to determine the significance of the effects of different heat treatment conditions on the organization structure and viscoelasticity of the puree. Different letters after the values ​​in the same row indicated that the index had significant differences under different heat treatment conditions, p < 0.05; finally, Origin 2022 (OriginLab, MicroCal, USA) was used to analyze the organization particle area, organization particle perimeter, η 50 Pearson correlation analysis was performed on G′ and G″, and the results were expressed as correlation coefficients.

[0017] Spectra and spectral data acquisition: A single integrating sphere system was used to measure the optical properties of all fruit puree samples in the visible-near infrared (Vis-NIR) and short-wave near infrared (SWIR) bands, where the visible-near infrared band ranges from 400-1050nm and the short-wave near infrared band ranges from 900-1650nm. Under light-proof conditions, the fruit puree of each sample was placed in a quartz dish with an inner diameter of 40mm long, 40mm high, and 1.9mm wide. By adjusting the position of the light source and the sample and enabling the system to switch between reflection mode and transmission mode, the absorption coefficient (μ) of the fruit puree was calculated using the inverse doubling integration method (IAD). a ) and the reduced scattering coefficient (μ s ′). Each sample was measured three times in two different wavelength ranges, Vis-NIR and SWIR. The calculation of reflectance (R) and transmittance (T) needs to include reference noise and dark noise, as shown in equations (1) and (2).

[0018]

[0019]

[0020] Where Rs, Rd, and Rt are the reflection intensities of the sample, dark noise, and reference noise, respectively; similarly, Ts, Td, and Tt represent the transmission intensities of the sample, dark noise, and reference noise, respectively; R and T are then used in IAD 3.9.10 to calculate the optical parameters (μ a and μ s ′).

[0021] Spectral data set: a total of 648 spectra, including: 108 absorption coefficients in the visible-near infrared band (Vis-NIR-μ a ), absorption coefficients in 108 short-wave near-infrared bands (SWIR-μa ), 108 short-wave near-infrared bands of reduced scattering coefficients (Vis-NIR-μ s ′), reduced scattering coefficients in 108 short-wave near-infrared bands (SWIR-μ s ′); then, by combining the same optical properties in the visible-near infrared band and the short-wave near infrared band (μ a or μ s ′), forming the optical properties under the full band (FW) (400-1650nm), including 108 absorption coefficients under the full band (FW-μ a ), 108 full-band absorption coefficients (FW-μ s ′).

[0022] 2. Spectral preprocessing and model construction

[0023] use R2016a (The MathWorks, USA) was used to construct a partial least squares (PLS) regression model. Before building the model, all spectral data (including 108 light absorption spectra and 108 light scattering spectra in the visible-near infrared and short-wave infrared regions) were preprocessed by standard normal variable (SNV) and smoothing (Savitzky-Golay method, gap = 20, polynomial order = 2). Figure 2 As shown in the figure, the puree structure, viscoelastic parameters and spectral signals under heat treatment time of 6, 10, 14, 18 and 22 min were used as calibration set, and the puree structure, viscoelastic parameters and spectral signals under heat treatment time of 8, 12, 16 and 20 min were used as validation set. The root mean square error of the calibration set (RMSEC), the root mean square error of the validation set (RMSEV), the prediction accuracy of the calibration set (R c 2 ) and the prediction accuracy of the validation set (R v 2 ) and the number of latent variables (LVs) were used to evaluate the predictive performance of the regression model.

[0024] 3. Analysis of the changing rules of puree structure and viscoelastic parameters during thermal processing

[0025] like Figure 3 As shown in Table 1, the particle circumference, particle area, apparent viscosity, elastic modulus and viscous modulus of apple puree under different heat treatment conditions have different changes. At 6, 10, 14 and 22 minutes of heat treatment time, the particle area and particle circumference of the puree showed significant differences (p < 0.05), and showed a downward trend with the increase of heat treatment time ( Figure 4a). Apparent viscosity of puree during different heat treatment times η 50 The viscosity of the puree decreased gradually from 7.11 Pa·s to 1.42 Pa·s (p<0.05), indicating that the apparent viscosity of the puree was lost during the heating process ( Figure 4 b). In addition, with the extension of heating time, the elastic modulus G′ and the viscous modulus G″ of the puree decreased significantly (p<0.05) ( Figure 4 c. Figure 4 d). Correlation analysis is shown in Table 2. The viscosity and elastic properties of apple puree are highly correlated with the particle area and perimeter, and the correlation coefficient (r) is greater than 0.80 (p < 0.001), indicating that the reduction of puree particles during thermal processing is an important reason for the decrease in puree viscosity and viscoelasticity.

[0026] Table 1 Microstructure and viscoelasticity of apple puree under different heat treatment conditions

[0027]

[0028] Note: Different letters after the values ​​in the same row indicate that the index has significant difference under different heat treatment conditions, p < 0.05.

[0029] Table 2 Correlation analysis between particle structure and viscoelasticity of applesauce

[0030]

[0031] 4. Comparative analysis of fruit puree structural parameters and viscoelasticity prediction based on optical properties

[0032] The predicted organizational structure and viscoelastic parameters of the puree based on different wavebands and optical properties are shown in Table 3, where the mean and standard deviation represent the numerical distribution of the index in the prediction set, R c 2 represents the prediction accuracy of the model for the calibration set, RMSEC represents the average prediction deviation of the model for the calibration set, and R v 2 RMSEV represents the average prediction error of the model for the validation set. c 2 With R v 2 The larger the LVs, and the smaller the RMSEC and RMSEV, the better the prediction effect of the model. The smaller the LVs, the fewer the number of latent variables used in the model, and the simpler and more reliable the model. In Table 3, in the PLS regression model based on optical properties to predict the structural parameters of fruit puree (tissue particle area and tissue particle perimeter), μ s The prediction effect of particle area and particle perimeter based on FW-μ is obviously better than that based on absorption coefficient.s The PLS regression model constructed by ′ can best predict the particle area of ​​fruit puree (R v 2 =0.88, RMSEV = 0.03) and particle circumference (R v 2 =0.86, RMSEV=0.16), the overall results are satisfactory. Figure 5 a), μ in the bands of 400-420, 423-447, 450-476, 1046-1050, 1460-1479 nm s ′ has a higher contribution in predicting puree structure.

[0033] For the viscoelastic parameters of fruit puree, in different PLS regression models, based on Vis-NIR-μ s The PLS model constructed by ′ was used to evaluate the viscosity of puree (R v 2 =0.84, RMSEV = 0.84) and elastic modulus (R v 2 =0.84, RMSEV=2.56) where μ in the 400-405, 426-434, 451-460, 1039-1050nm bands s ′ has a higher contribution to the model ( Figure 5 b. Figure 5 c), and based on Vis-NIR-μ a The constructed PLS model is effective in evaluating the viscosity modulus of puree (R v 2 =0.80, RMSEV=0.44) showed the best performance, among which μ a The contribution to this model is high ( Figure 5 d) In addition, based on Vis-NIR-μ a The constructed PLS model was used to evaluate the viscosity of puree (R v 2 =0.84, RMSEV = 0.85), elastic modulus (R v 2 =0.80, RMSEV=2.65), and based on Vis-NIR-μ s The constructed PLS model is effective in evaluating the viscosity modulus of puree (R v 2 =0.78, RMSEV=0.45) also obtained similar results to the best model. Therefore, the light absorption and scattering characteristics in the visible-near infrared band have the potential to evaluate the viscoelasticity of fruit puree.

[0034] Table 3 Pre-purée structure and viscoelastic parameters using optical properties and PLS regression method

[0035]

[0036]

[0037] In summary, the PLS model evaluation based on light absorption and scattering characteristics shows that using μ a and μ s ′ By characterizing the light absorption and scattering properties of the puree, the organizational structure and viscoelasticity of apple puree during thermal processing can be directly predicted.

[0038] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the above embodiments do not limit the present invention in any form, and any technical solution obtained by equivalent replacement or equivalent transformation falls within the protection scope of the present invention.

Claims

1. A method for evaluating the structure and viscoelasticity of fruit puree based on optical properties, comprising the following steps: S1. Select fresh "Fuji" apples. After sorting and cleaning each apple, weigh 100g of apple pulp, heat it at 100℃ for 6, 8, 10, 12, 14, 16, 18, 20, and 22min, and perform thermal crushing at a mechanical crushing rate of 3000rpm / min for 30s to prepare different puree samples. Under the above 9 mechanical heat treatment conditions, 4 batches of apple puree were processed continuously, and a total of 36 groups of samples were prepared and placed in sealed plastic bags for testing. S2. Using a single integrating sphere optical measurement system, 36 groups of puree samples were placed in quartz cuvettes, and the absorption coefficient (μ) of each group of puree samples in the visible-near infrared (Vis-NIR, 400-1050nm) and short-wave near infrared (SWIR, 900-1650nm) bands was measured in turn. a ) and the reduced scattering coefficient (μ s ′), repeated the measurement three times, and combined the absorption coefficient or reduced scattering coefficient in the two bands (Vis-NIR / SWIR) to obtain the optical characteristic data in the full band (FW, 400-1650nm). A total of 648 spectral data (9 heat treatment conditions × 4 processing batches × 2 bands (Vis-NIR / SWIR / FW) × 2 optical characteristics (μ a / μ s ') × 3 repeated measurements). S3. Use a dynamic shear rheometer to perform apparent viscosity curve test and amplitude sweep test to calculate the viscosity of 36 groups of puree samples at a shear rate of 50s -1 The apparent viscosity value η under 50 , as well as the average elastic modulus (G′) and the average viscous modulus (G″) in the linear viscoelastic region. S4. Use a high-resolution camera to capture two-dimensional images of the puree tissue particle structure evenly dispersed in the culture dish. After the image is automatically thresholded and binarized, use Image J software to analyze and obtain the characteristic parameters of the puree tissue particle structure (particle area, particle circumference). S5. After preprocessing the 648 spectral data with standard normal variables (SNV) and smoothing (Savitzky-Golay derivative), the average of three repeated measurements of the spectrum was taken to obtain 216 average spectra. Among them, the average spectra (μ a / μ s ′) as the model training set, including the average absorption coefficients of 20 visible-near infrared bands (Vis-NIR-μ a ), the average reduced scattering coefficient in 20 visible-near infrared bands (Vis-NIR-μ s ′), the average absorption coefficient in 20 short-wave near-infrared bands (SWIR-μ a ), the average reduced scattering coefficient in 20 short-wave near-infrared bands (SWIR-μ s ′), the average absorption coefficient of 20 full-bands (FW-μ a ), the average reduced scattering coefficient (FW-μ s ′); average spectrum (μ a / μ s ′) as the model validation set, including 16 average Vis-NIR-μ a 、16 average Vis-NIR-μ s ′、20 average SWIR-μ a 、16 average SWIR-u s ′、16 average FW-μ a 、16 average FW-μ s ′. S6. Using Matlab software and partial least squares regression (PLS) toolbox, a prediction model of the structure and viscoelasticity of fruit puree based on optical properties was established. The root mean square error of correction (RMSEC), root mean square error of verification (RMSEV), and prediction accuracy of the corrected data set (R c 2 ) and the prediction accuracy of the validation dataset (R v 2 ) and the number of latent variables (LVs) were used to evaluate the prediction performance of the PLS model, and the relevant bands were screened out according to the loading values ​​at each wavelength.

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