1. Introduction
Beef is a popular food in the human diet, and its rich amino acid and protein composition is close to human needs [
1]. With the rapid development of social economy and the continuous improvement in life quality, meat quality plays an increasingly important role in determining the value of meat products, and more and more consumers are attracted to high-quality meat [
2]. As the most critical factor affecting the quality of meat products, flavor not only affects the taste of food and the absorption of nutrients, but also determines the consumers’ purchase desire and intake intention to a certain extent [
3]. Free amino acids (FAA) are the main taste substances in meat products. Their type and content have an important impact on meat quality, antioxidant activity, and nutritional value. They can also be used as precursors for the Maillard reaction and Strecker degradation reaction with reducing sugar, affecting the overall flavor of the food system [
4]. Different FAAs have different taste characteristics. They have a low taste threshold, strong taste ability, and five basic taste senses: sour, sweet, bitter, salty, and umami [
5]. Among them, alanine (Ala), as the most simple-flavored amino acid among FAAs, has become the main sweet amino acid in meat products due to its low hydrophobicity, and the umami of food will be directly affected by its content [
6]. When Ala coexisted with taste substances such as glutamic acid and ornithine in food, it can produce a synergistic effect and provide strong umami for meat products [
7]. In addition, Ala also plays a variety of important physiological roles, including improving the immune system, preventing and treating vascular diseases, and participating in growth and metabolism [
8]. When too much or too little Ala is ingested, the absorption balance of human total amino acids might be affected, leading to nutritional imbalance and poor health [
9]. Therefore, it is of great significance to develop a rapid, nondestructive, and noncontact quantitative method for the determination of Ala content in beef.
At present, high-performance liquid chromatography and automatic amino acid analysis are often used for the physical and chemical detection of FAA [
2,
3,
4]. These methods have the advantage of high precision in detecting the composition and content of amino acids. However, their disadvantage is that sample pretreatment is complex, harmful, and polluting, and the integrity of the sample is damaged [
10]. Therefore, the rapid detection requirements in the beef mass production process cannot be met. In previous investigations, the combination of several nondestructive rapid measurement methods and chemometric methods have been applied in the assessment of amino acid content, including visible near–infrared spectroscopy, near–infrared (NIR) spectroscopy, Fourier infrared spectroscopy, and nondestructive magnetic resonance imaging [
11,
12,
13,
14]. However, these studies mainly focused on the evaluation of research objectives concerning soybean, daqu, tea, potato, and ham [
11,
12,
13,
14,
15], and detection indicators such as amino acid nitrogen [
12], total amino acid [
15], and total volatile basic nitrogen (TVB-N) have been emphatically discussed [
16]. In addition, hyperspectral imaging (HSI) technology is more widely focused in predicting other meat-related quality attributes, especially nutritional attributes (fatty acid, protein, and intramuscular fat), technical attributes (pH and water holding capacity), sensory attributes (tenderness, color, hardness, gumminess, and chewiness), freshness attributes (thiobarbituric acid reactive substances (TBARS), total biogenic amines (TBA), and myoglobin), and microbial attributes (total viable count) of meat in different parts, types, and places of origin [
17,
18,
19,
20,
21,
22,
23,
24,
25]. Notably, Cheng et al. [
23,
24] reported that NIR–HSI had great application potential in evaluating the content of meat quality (TBA, TBARS, and fat oxidation). Through comparison, it was found that molecules with greater contributions could be detected more easily than those with smaller contributions. The above research provides the possibility to reveal NIR–HSI prediction of Ala content in meat and meat products.
However, beef, as a complex food item, has interactions among various components (proteins, amino acids, lipids, and carbohydrates) [
26]. This makes it difficult to extract the NIR spectral information of the meat, and the spectral signal presents overlapping and complexity [
27]. As a result, quantitative analysis by NIR has relied heavily on the application of chemometric analysis to relate the subtle spectral changes to the variations in concentrations of certain components in the analyte [
28]. In previous studies, the derivative method, weight value method, principal component analysis, and feature variable extraction were the main spectral response analysis methods for studying the spectral characteristic variables of objects [
29,
30]. It usually requires a lot of experiments to identify sensitive variables and build a stable model effect. This is a time-consuming and iterative process, the results of which vary with experience and the chemometric methods used. Thus, a better understanding of NIR spectra and a more accurate spectral band division is conducive to the establishment of a more robust NIR quantitative model. In recent years, two–dimensional correlation spectroscopy (2D–COS) has been applied to HSI research by some researchers to improve spectral resolution by extending one–dimensional spectral signals to the second dimension [
31]. By this means, the changes in subtle spectral features are analyzed and the relationship and change order of various groups are revealed [
32]. This technique has been successfully applied to spectral interpretation and spectral band allocation of the lipid oxidation of meat, the damage of myofibrils, and the spectral interpretation and distribution of protein secondary structure changes in meat [
24,
26]. In addition, Dong et al. [
33] reported that the use of 2D–COS to select the NIR–HSI continuous sensitive interval and the establishment of a deep learning algorithm have great potential to improve the accuracy of the model. This provides a new direction for our research. As far as we know, a feasibility study using NIR–HSI combined with 2D–COS analysis to detect Ala content in beef has not been previously reported.
Therefore, this study is the first to explore the feasibility of NIR–HSI combined with 2D–COS analysis in detecting Ala content in beef. The specific objectives were as follows: (i) NIR–HSI (900–1700 nm) was used to collect the spectral information of beef samples, the segment threshold method was used to select the sample region of interest (ROI), and the Monte Carlo (MC) method was used to eliminate abnormal value information; (ii) the determination of the change order of characteristic peaks related to Ala content and local sensitive intervals was achieved by analyzing synchronous and asynchronous 2D–COS; (iii) the determination of the best characteristic variables of the global and local spectral intervals based on the weight algorithm (competitive adaptive reweighted sampling (CARS) and regional coefficient (RC)) were studied; (iv) simplified linear partial least squares regression (PLSR), nonlinear least squares support vector machine (LSSVM), and artificial neural network (ANN) Ala prediction models were developed; (v) the optimal characteristic variables and models obtained were used to characterize the visual distribution of Ala content. The research results were expected to further improve the accuracy of NIR–HSI technology in detecting meat quality indicators. A graphical representation of the proposed method is illustrated in
Figure 1.
2. Materials and Methods
2.1. Sample Preparation
The samples were collected from three parts (longissimus dorsi (LD), foreleg (FD), and hind leg (HD)) of 20 cattle in Ningxia, China. The samples were vacuum packed and stored in a portable refrigerated incubator, and were transported to the Meat Processing and Quality Safety Control Laboratory of Ningxia University. The oil and fascia in the fresh samples were removed and cut into 40 mm × 40 mm × 20 mm (L × W × T). All samples were vacuum packed and stored in a 4 °C refrigerated room. In order to ensure the reliability and universality of the model, the samples were collected following four slaughter batches (30 samples were obtained from each part of each batch). Finally, 360 beef samples were obtained, and their spectral data and chemometric data were measured at the same time.
2.2. Hyperspectral Image Correction and Parameter Determination
The NIR–HSI (900–1700 nm) system was used to collect spectral images of beef samples. The HSI system can continuously acquire 256 spectral bands with a spectral resolution of 5 nm. The HSI system was mainly composed of five parts, including an HSI spectrometer, four 35 W tungsten halogen lamps, a CCD camera, an electronic displacement platform, and a computer. The best debugging parameters were optimized during preliminary experiments because diffuse reflections of the light source may be caused by the color, texture, and shape of the beef sample. The best debugging parameters were as follows: the object distance was 360 mm, the steady current of the light source was set to 6.0 A, the electric control displacement speed was 20 mm/s, and the exposure time of the camera was 30 ms. In order to reduce the uneven distribution of image light source intensity and the existence of a dark current in the sensor, black and white correction was required for the obtained hyperspectral image. The formula used was as follows:
where
A is the original spectral image of the sample;
B is the all-white calibration image;
S is the all-black calibration image;
R is the calibrated spectral image. The all-black calibration image
S was obtained by covering the camera lens (almost 0% reflectance), and the all-white calibration image
B was obtained using a white board made of polytetrafluoroethylene (>99% reflectance). The ROI of the HSI was extracted from the spectral information of the sample using the segmented threshold method (set at 0.16) with ENVI software.
2.3. Measurement of the Content of FAAs
Sample pretreatment: minced meat sample (2.00 g) was weighed, 0.02 mol/L hydrochloric acid was added, and the sample was then placed in a 10 mL centrifuge tube for homogenization. After ultrasound (30 min), centrifugation (4000 r/5 min), and activation (C18), 5.00 mL methanol and 5.00 mL water were added, respectively. After filtration, 2.5 mL of solution was absorbed and 1.50 mL hydrochloric acid of 0.02 mol/L was added. After passing through the column, 0.02 mol/L hydrochloric acid was used to dilute the solution to 5.00 mL. After uniform mixing, the solution was centrifuged (10,000 r/10 min) after standing (15 min), and then filtered through a membrane of 0.45 μm pore size for analysis. By comparing the retention time and peak area of each amino acid standard, qualitative and quantitative analysis of each amino acid was carried out.
Analytical parameters: the chromatographic column used was a sulfonic acid cation resin separation column (4.6 mm × 60 mm). The detection wavelength was 440 nm and 570 nm, respectively; the injection volume was 20 μL. The reaction temperature was 135 ± 5 °C. The separation column temperature was 57 °C.
2.4. Analysis of Two–Dimensional Correlation Spectra
As an advanced spectral analysis method, 2D–COS is particularly suitable for exploring the structural changes and interactions of complex systems under external disturbances from the molecular perspective [
31]. Compared with traditional one–dimensional spectral analysis, 2D–COS has a strong simplification effect for complex spectra containing multiple overlapping peaks. At the same time, it is extended on the basis of the original one–dimensional spectrum, significantly improving the resolution of the original spectrum [
32]. With the help of a peak correlation diagram, the assignment and interaction of peaks in the system were judged, and the change order of peak position under external disturbance was obtained [
26]. In this study, Ala was used as the external disturbance condition, and the dynamic spectrum
(
v,
d) caused by the system in the external disturbance range (1~
T) was defined as:
where
(
v) is the reference spectrum of the system, which was usually set as the average spectrum, and was defined as:
where
(
v,
d) data are expressed in discrete form in actual measurement. The following vector forms were commonly used:
The two–dimensional correlation intensity
X (
v1,
v2) indicated the function of the spectral intensity changes
(
v,
d) of the spectral variables
v1 and
v2, and was compared in the external disturbance variable interval. The correlation function was used to calculate the intensity change at two independent spectral variables,
v1 and
v2, so that
X (
v1,
v2) could be converted into the plural form:
According to the 2D–COS theory of Noda et al. [
31,
32], the mutually perpendicular real parts and imaginary parts of the complex number were called the synchronous correlation strength and asynchronous correlation strength, respectively, and the strength changes of the two were directly related to the change in d value. We then converted the dynamic spectrum from the external interference domain to the frequency domain via Hilbert–Noda change, and finally, 2D–COS was obtained. Its two–dimensional correlation synchronous spectrum was expressed as (6):
The expression of the two–dimensional correlation asynchronous spectrum was (7):
where
N is the
T-order square matrix (
T is the spectral number), which was called the Hilbert–Noda matrix; the matrix formula was (8):
2.5. Analysis Rules of Spectral Peak
The synchronous correlation spectrum characterizes the simultaneous or coincidental changes in spectral intensities measured at spectral variables of
v1 and
v2. In the atlas, it was symmetrical along the diagonal direction, its autocorrelation peak appeared on the diagonal, and the cross peak appeared outside the diagonal. In the synchronous spectrum, the intensity of the automatic peak was always positive, representing the overall degree of the dynamic change in spectral intensity under the corresponding number of bands. It is worth noting that there were positive and negative cross peaks in the synchronous spectrum. If the cross peaks of the two bands were positive, it meant that the spectral intensity of the corresponding wave number increased or decreased simultaneously under external interference. When the opposite value was negative, it meant that the spectral intensity corresponding to the two wave numbers increased one and decreased the other. In the asynchronous two–dimensional correlation spectrogram, the asynchronous or sequential (i.e., delayed or accelerated) changes in spectral intensity at the given wave numbers
v1 and
v2 were presented. Its asynchronous graph was asymmetric with respect to the diagonal, and only had cross peaks. The asynchronous cross peak only appeared when the spectral intensity of a given wave number changed out of phase. Using this feature to analyze the overlapping peaks with different sources in the spectrum had a significant role in judging the change order of the characteristic peaks in the process of external interference. The direction and order of strength change determined according to Noda rules are shown in
Table 1.
2.6. Extraction of Spectral Characteristic Wavelength
The complex and time-consuming properties of model training were caused by the high dimension of the data and the strong correlation between adjacent variables. Therefore, the selection of characteristic wavelength variables became a key step in spectral analysis, which was mainly used to simplify the model and eliminate data redundancy. The use of genetic algorithms, principal component algorithms, iterative algorithms, and weight algorithms to extract feature variables has been reported by a large number of researchers [
27,
28,
29]. In this study, two weight algorithms (CARS and RC) were used to select characteristic wavelengths to develop simplified models for the full spectral (FS) area and sensitive local areas selected from 2D–COS analysis. The purpose of this was to consider the proportion of data from the perspective of weight to analyze the appearance of characteristic variables more reasonably.
2.7. Visualization of the Ala Contents
Inversion of Ala content distribution was feasible because HSI had both image information and spectral information (combining two–dimensional imaging technology with one–dimensional spectral curve to form three–dimensional data). Based on the method of multivariate optimization model, the optimal simplified combination was selected, the weight coefficients of each pixel and the optimal correction model were calculated, and a matrix consisting of multiple predicted values was obtained. Then, the obtained matrix was refolded to generate the content distribution map [
30]. The Ala content of each pixel was expressed in different color scales. Therefore, the distribution of Ala content could be clearly inverted on the color map.
2.8. Model Establishment and Evaluation
In this study, three multivariate algorithms, including linear PLSR, nonlinear LSSVM, and ANN, were used to develop the detection model for quantitative analysis. PLSR is a multiple factor regression method. Firstly, the scores of the main factors were extracted from the spectral matrix X and the physical and chemical matrix Y, and the PLS was used to conduct the best precision regression for the main factors of X and Y, respectively. The principal component of spectral matrix X was directly related to physical and chemical parameters, and the linear relationship between spectral variables and physical and chemical parameters was used to the greatest extent. LS-SVM used the least squares linear equation as the loss function formula. The convex quadratic programming was solved by solving linear equations instead of traditional SVM, which reduced the training time and computational complexity. ANN is a multilayer feedforward neural network characterized by forward signal propagation and backward error propagation. According to the error signal of forward propagation, the method of gradient descent was adopted for backward propagation, and the signal error was minimized through repeated forward and backward learning.
In order to establish the reliable accuracy of the validation model, the whole sample set data were divided according to a 3:1 ratio, based on the RS method. The proficiency and accuracy of the model were evaluated by analyzing statistical parameters. These included determination coefficients (R2), root mean square error (RMSE), and ratio of performance deviation (RPD). Generally, a good model should have higher R2 and RPD values and lower RMSE values.