Near-infrared reflectance spectroscopy for the prediction of chemical composition in walnut kernel

ABSTRACT

In the present work, 116 samples were collected and near-infrared reflectance spectroscopy prediction model for determination of moisture, protein, and fat contents of walnut meal were obtained and evaluated. All the samples were analyzed based on the chemical methods. Meanwhile, they were scanned to obtain their near-infrared reflectance spectrum in the wavelength range of 570–1840 nm. Several preprocess treatments including scattering pretreatments, mathematical pretreatments, and aggression methods were optimized to increase the accuracy of the calibration models according to the coefficient of determination for calibration (R_c²) and the cross-validation (one minus the variance ratio, 1-VR), and the standard error of calibration and cross-validation. The results showed modified partial least square loading was the better aggression method to predict the moisture, proteins, and fats in walnut kernel. The calibration equations obtained indicated that near-infrared reflectance spectroscopy had excellent predictive capacity for the three components with R_c² = 0.965, standard error of calibration = 0.052 for moisture, and R_c² = 0.967, standard error of calibration = 0.191 for proteins, and R_c² = 0.979, standard error of calibration = 0.171 for fats, respectively. The external validation further confirmed the robustness and reliability of the near-infrared reflectance spectroscopy prediction models with the correlation coefficient of actual and predicted values (R²) = 0.952, ratio of performance deviation = 4.14, the standard error of prediction =0.058 for moisture, and R² = 0.977, ratio of performance deviation = 5.55, standard error of prediction = 0.182 for proteins, and R² = 0.990, ratio of performance deviation = 8.64, standard error of prediction = 0.191 for fats, respectively. Near-infrared reflectance spectroscopy is a reliable technology to predict these constituents in walnuts.

KEYWORDS:

• Walnut
• Near-infrared spectroscopy
• Chemical composition
• Moisture
• Protein
• Fat

Introduction

The walnut (Juglans regia L), native in southeastern Europe, Asia Minor, India, and China, is a member of Juglandaceae family.^[1–3] The walnut tree is not only a commercial crop, but its leaves, stems, barks, pericarps, flowers, and fruits, are all the traditional Chinese medicinal materials for different uses.^[4] The walnut seed (kernel) has high nutritional value, particularly rich in oil (52–70% of the walnut kernel weight), which contains about 7% saturated, 20% monounsaturated, and 73% polyunsaturated fatty acids (PUFAs). The major PUFA are linoleic (omega-6) and α-linolenic (omega-3) acids.^[5–7] In recent decades, a great of research focuses on the walnut oil due to its nutritional benefits to health.^[8] It is well-documented that the consumption of walnut oil can improve serum lipid profiles with reduced low-density lipoprotein cholesterol and total plasma cholesterol, and increased high-density lipoprotein cholesterol.^[9–11] Dietary intake walnut oil has been well-evidenced to reduce the potential risk of the coronary heart disease.^[12,13] These healthy functions may be due to the fatty acid profile of walnut oil, especially, omega-3 and omega-6 PUFA.^[14] Walnut seed is also rich in proteins, carbohydrates, fiber, and minerals.^[15,16] In addition, the walnut was reported to have some minor components (e.g., phenols, tocopherols, and phytosterols), which exhibit high antioxidant capacity.^[17,18].

The world production of walnuts exceeds 1,500,000 metric tons.^[4] China is the major producers, accounting for about 25% the total. Also, Walnut is one of China’s traditional export products. Most often the quality of walnut is determinant to the basis for its purchase in the markets.^[4] Thus, it is necessary to determine the chemical compositions of walnut to ensure that the processors get the right quality of walnut from their suppliers. The fat, protein, and moisture contents are the three important quality parameters of walnut kernel. Traditionally, these parameters have been evaluated by chemically analytic methods in laboratories, such as the Soxhlet method, the Kjeldahl method, and the gravimetric method for the determination of the fat, protein, and moisture contents, respectively. However, these typically chemical methods are labor and time consuming, costly, sometimes even environmentally harmful and hazardous. For instance, hazardous organic solvent extraction is used for the measurement of the fat content.^[19–21] Additionally, they fail to achieve a quick response because of lengthy sampling procedures and preparation.^[22] Consequently, the efficiency of walnut quality control necessitates rapid, versatile, and highly effective methods for the instant determination of all these parameters. With the recent development of microelectronics, near-infrared reflectance spectroscopy (NIRS) has become a powerful tool to estimate the quality attributes due to its capacity for fast and non-destructive, on-line, or at-line analysis of a high variety of food products.^[23,24] Many studies have confirmed that NIRS can be applied to predict the chemical compositions, such as the moisture, protein, and fat contents in food materials and products. For example, López and coworkers have reported that the NIRS technique has been successfully used for the rapid analysis of moisture and fat content in potato products.^[25] Also, Rodriguez-Otero and coworkers have reviewed the analysis of dairy products by NIRS, and concluded that NIRS is an adequate technique for the analysis of major components (e.g., fats, proteins, and moisture) in dairy products without sample pretreatment.^[26] Moreover, Salguero-Chaparro et al. have suggested that NIRS could be used as an on-line determination method to monitor and control the olive quality parameters such as oil content, moisture and free acidity, and provide real time information at the reception in the mill about the quality and composition of the olives that will be used in the olive oil extraction process.^[27] Recently, some experts have utilized NIRS to estimate the quality of nuts. For instance, Roberto Moscetti et al. have applied visible (Vis)/NIRS to detect flaws in hazelnut kernels and have found that it is a rapid, online detection system.^[28] Furthermore, Pannico et al. have evaluated the feasibility of NIRS to detect flawed kernels and estimate lipid oxidation in in-shell and shelled hazelnuts, and have suggested that NIRS is a sufficiently accurate and fast technique to detect the degree of kernel defect and the estimation of lipid oxidation in both in-shell and shelled hazelnuts.^[29] However, to the best of the author’s knowledge, these studies would mainly be interesting in using NIRS to predict the quality of nuts and their lipid fraction (such as, saturated fatty acids, monounsaturated fatty acids, and PUFAs), fewer studied have focus on the application of NIRS into the prediction of walnut compositions. Therefore, the object of the present work was to evaluate NIRS as a potential tool for measuring walnut quality parameters including moisture, protein, and fat contents.

Materials and methods

Walnut samples

Four different walnut cultivars (e.g., hetian, xiangling, longboxiang1#, jinlong1#) were sampled from Xinjiang, Shaanxi, Gansu, and Shanxi provinces in China, respectively, and the total sample number was 116. The walnut seeds were gathered during the harvesting time (August–November in 2014). The samples were milled, packed in plastic bags, sealed, and stored at 4°C until their chemical and spectroscopic analyses were carried out.

NIRS analysis

Near-infrared spectra were collected using an InfraXact spectrophotometer (Foss Electric A/S, Hillerød, Denmark). The instrument works in reflectance mode with a moving grating monochromator scanning the region from 570 to 1850 nm with an interval of 2 nm. About 60 g of ground samples were placed in a 30 mm × 75 mm i.d. sample dish. The spectrum of each sample was the average of 16 scan locations and was recorded as log (1/R).

Chemical analysis

The chemical compositions of walnut kernels, such as moisture, protein, and fat contents, were determined based on the standard Association of Official Analytical Chemists (AOAC) procedures.^[30] The walnut moisture was measured by oven-drying at 105°C for 24 h. Total protein content was measured by the Kjeldahl method, and calculated with a nitrogen conversion factor of 6.25. Crude fat was determined by the Soxhlet extraction with petroleum ether. Prior to statistical analysis, the triplicates of each sample were averaged.

NIRS calibration

NIRS calibration models were established by using WinISI Ⅲ software version 1.5 (Infrasoft International, Port Matilda, PA, USA) to collect and analyze the data. In order to improve the accuracy of the calibration, before the analysis of the samples, the whole dataset underwent the detection of anomalous spectra by using the Mahalanobis distance (namely, Global H statistics, GH). According to Williams and Norris,^[31] GH could provide information about the difference between a sample spectrum and the average spectrum of the dataset; samples with GH > 3 were regarded as outliers and were then removed from the dataset. And the final population of samples available for research was 114. And then the remaining dataset was then manually split into two groups: the calibration group (also used for the internal cross-validation, formed by 98 samples) and the external validation group (formed by 16 samples). To optimize the calibration equations, a variety of scattering pretreatments including standard normal variate + detrending (SNV+DT), SNV only, DT only, standard multiplicative scatter correction (SMSC), weight multiplicative scatter correction (WMSC), and inverse multiplicative scatter correction (IMSC) were performed to correct the influence of scatter phenomena and the sample particle size on NIRS spectra and the path-length variations. Also, different derivative mathematical pretreatments were employed to decrease the noise effects. The derivative pretreatments evaluated for each calibration equation were coded as follows: “0,4,4,1,” “1,4,4,1,” “2,4,4,1,” “3,4,4,1,” and “4,4,4,1,” where the first digit is the number of the derivative, the second one is referred to the gap over which the derivative is calculated, the third one is the smoothing segment, and the last one is the secondary smoothing segment.^[32] In addition, several aggression methods including modified partial least square loadings (MPLS), partial least square loadings (PLS), and principal component analysis (PCA) were also evaluated. For the three walnut compositions (e.g., moisture, proteins, and fats), the best models were selected on the basis of the highest coefficient of determination of calibration (R_c²) and the internal cross-validation (one minus the variance ratio, 1-VR), and the lowest standard error of calibration (SEC) and internal cross-validation (SECV), and the smallest difference between SEC and SECV.^[21] To compare the prediction ability of calibration equations among different parameters, the correlation coefficient of actual and predicted values (R²) obtained in the external cross-validation set was evaluated, and the ratio performance deviation (RPD) was calculated by dividing the standard deviation (SD) of the reference data also from in the external cross-validation set by the standard error of prediction (SFP). It has been suggested that RPD values >3 are considered good for screening purpose; RPD values >5 are good for quality control, whereas RPD values >8 are considered excellent for all analytical tasks.^[33]

Results and discussion

Walnut chemical compositions

The walnut varieties used in present work were chosen from the four major walnut-producing regions in China based on the need to cover a range of walnut composition contents as wide as possible. A statistical overview of the range, average, SD, and coefficient of variation (CV) for moisture, protein, and fat contents determined by conventional methods are presented in Table 1. The measured values varied considerably in the three parameters for all examined samples as shown by the range and CV given in Table 1. Also, it could be seen that variation in fat content was smaller than those observed in moisture and protein contents. The overall determined fat content varied from 64.33 to 72.10%, protein content from 14.08 to 19.23%, and moisture from 3.08 to 4.08%. In general, the moisture, protein, and fat contents exhibited by the samples in this work are in consistence with those reported in the literature.^[14,16] The distributions of the obtained chemical values for each component determined by conventional methods are also demonstrated that the distributions of the three parameters were close to the normal distribution (data not shown), indicating that the overall situation of the collected samples was considered suitable for establishing NIRS calibration equations.

Table 1. Summary of chemical composition of analyzed walnut samples (data expressed as %, n = 116).

	Range	Mean	SD	CV
Moisture	3.08–4.08	3.48	0.28	8.5
Protein	14.08–19.23	15.94	1.11	6.96
Fat	64.33–72.10	67.85	1.76	2.59

SD: standard deviation of data; CV: coefficient of variation.

Walnut spectral profile

All the samples were automatically undertaken spectral extraction. The typical NIRS of the walnut samples from different agricultural areas in China are shown in Fig. 1a. Spectral profiles of all the examined samples exhibited a similar pattern over the whole NIRS range tested in the study, which indicated similar components in the evaluated walnut varieties. The most prominent attributes that affect the NIRS include the C-H stretching overtone related to fat, O-H stretching overtone associated to water, and N-H stretching overtone originating from amides and amines related to the proteins.^[31,34] The absorption peaks of spectra shown in Fig. 1a are related to the main components of walnut kernel. For example, the main absorbance wavelength regions for fats are 1151–1248 nm and 1676–1776 nm in the near-infrared area.^[35] The characteristic absorption bands at 1300–1450 nm are due to the overtones of O-H, corresponding to the moisture content in the samples.^[22] The wavelength range of 1420–1700 nm is related to the N–H overtone associated with the protein content.^[36] However, the absorbance bands in the NIRS range tend to be broad, not sharp, and basically overlap in most parts cross the NIRS wavelength region due to the complex compositions in walnut, making the assignment of absorption peaks a complex task. Consequently, the second derivative mathematical treatment was applied to improve the resolution of spectrum displaying distinct peaks primarily associated with the moisture, protein, and fat contents in walnut seeds and the second derivative spectra obtained from the original NIRS above are shown in Fig. 1b. The second derivative spectra provided more information about the contributions resulting from specific compositions to the NIRS. For instance, the spectral peaks located at around 1021 nm is associated with N–H stretching overtone related to proteins^[37] and the peak at about 1685 nm assigned to the first overtone of C-H, is directly correlated with protein contents.^[33] Band at 930 nm and 1151 nm are related to fat contents.^[38] In addition, the peak at 1412 nm is related to water.^[39] Other peaks corresponding to fat or protein contents are located at around 1209 nm, which is assigned to the C–H second overtone, at 1727 and 1764 nm, which are associated with the first overtone of the C-H stretching model.^[31,33]

Figure 1. A: Average NIRS spectra for different walnut samples; B: Second derivative spectra calculated from the spectra in Fig. 1a.

NIRS predict models

Amplification and Baseline shift often occur during the collection of near infrared spectra, which affect the accuracy of calibration models. The mathematical pretreatment and scattering pretreatment can eliminate the influence of these factors stated above on the target spectra. Thus, all the data preformed the derivative mathematical pretreatments including “0,4,4,1,” “1,4,4,1,” “2,4,4,1,” “3,4,4,1,” and “4,4,4,1.” In such a case, the MPLS regression method and SNV+DT scattering method were employed in order to be coherent with those pretreatments used in identifying outliers. The results showed that the calibration models for moisture, proteins, and fats obtained the highest R_c² and 1-VR, and the lowest SEC and SECV when the data of moisture, proteins and fats were treated with “4,4,4,1,” “2,4,4,1,” and “4,4,4,1,” respectively (data in Appendix A). Also, different scattering pretreatments such as SNV+DT, SNV, DT, SMSC, WMSC, and IMSC were also used to optimize calibration equation parameters of the three major components of walnut. In the case, the MPLS regression method and the best mathematical pretreatments of the three components mentioned above were applied. The best methods to scattering moisture, proteins, and crude fats were DT, SNV+DT, and SMSC, respectively, according to the highest R_c² and 1-VR, and the lowest SEC and SECV (data in Appendix B).

Additionally, based on the optimal mathematical and scattering methods, the data of the moisture, proteins, and fats were carried out multivariate analysis techniques such MPLS, PLS, and PCA, respectively, to obtain the optimal regression method. The best regression method for the moisture, protein, and crude fat was all the MPLS (data in Appendix C). It has been suggested that MPLS can create its factors by capturing the variation of spectral data as much as possible and actively exploiting the chemical values during the decomposition of the spectral dataset. The method decreases the effects of large but irrelevant spectral variations on the calibration models by balancing the chemical and spectral information. MPLS along with PLS and PCA was successfully utilized to develop the calibration models for prediction of varied food compounds.^[21,39,40] In our study, the calibration models for moisture, protein, and oil contents based on the optimal mathematical pretreatments, scattering processing, and regression method are summarized in Table 2. As can be seen in Table 2, the R_c² values corresponding to the predictions for the moisture, protein, and fat contents were higher than 0.9 (0.965, 0.967, and 0.979, respectively), and the SEC values were also low (0.052, 0.191, and 0.171, respectively), indicating the excellent quantitative correlation between predicted and measured moisture, protein and crude fat contents. In addition, as shown in Table 2, the excellent predictive capacities of the selected equations corresponding to these three parameters were further confirmed by higher 1-VR (0.937, 0.956, and 0.939 for moisture, proteins, and fats) and the low differences found between the SEC and SEVC.

Table 2. Data pretreatments and calibration model for quality parameters (n = 98).

	Mathematical pretreatments	Scattering pretreatments	Aggression methods	SEC	Rc^2	SECV	1-VR
Moisture	4,4,4,1	DT	MPLS	0.052	0.965	0.071	0.936
Protein	2,4,4,1	SNV+DT	MPLS	0.191	0.967	0.222	0.956
Fat	4,4,4,1	SMSC	MPLS	0.171	0.979	0.291	0.937

SEC: standard error of calibration; R_c²: coefficient of determination of calibration; SECV: standard error of the internal cross-validation; 1-VR: coefficient of determination of the internal cross-validation; DT: detrending only; SNV+DT: standard normal variate + detrending; SMSC: standard multiplicative scatter correction; MPLS: modified partial least square loadings.

External validation of the quantitative NIRS models

The predictive NIRS models were applied to an external sample set (n = 16) not used in developing the original calibration models for walnut moisture, proteins, fats, thus the set is completely independent. Results for the prediction expressed as R², SEP, and RPD are shown in Fig. 2. The validation for moisture, proteins, and fats had R² values of 0.952, 0.977, 0.990, respectively, and SEP 0.058, 0.182, and 0.191, respectively. R² values above 0.9 are generally considered to offer reasonably good quantitative information for the evaluation of the predictive accuracy of models. Also, based on the suggestion by Ritthiruangdej and co-workers,^[33] the values for RPD shown by the predictive models in the work, indicated useful for screening purposes (4.14 for moisture), good for quality control (5.55 for proteins), and excellent for analytic applications (8.64 for fats). Therefore, our R² and RPD values obtained in the study indicated the robustness and reliability of the NIRS prediction models in the analysis and quality control of walnuts.

Figure 2. NIRS-predicted values versus chemical values in the external validation set for A: moisture; B: protein content; and C: fat content.

It has been suggested that the paired t-test can be used to detect if the mean of the differences of each pair of samples determined by the chemical methods and by NIRS differs significantly from zero. Therefore, the paired t-test was applied to compare the results obtained by NIRS for moisture, proteins, and fats in the external validation set with those achieved by the chemical methods.^[41] The paired t-test demonstrated that the null hypothesis was retained: the results obtained from the two methods did not have significantly difference (p > 0.05, data not shown).

For moisture, the results obtained in present work were quite well comparable with the accuracy of on-line equations for moisture in intact olives reported by Salguero-Chaparro and coworkers (R² = 0.87; RPD = 2.76).^[27] The current research also showed a quite lower SEP value (0.057 %) for moisture content than that recorded by Cayuela and co-workers (1.52%).^[42] In addition, the developed predictive model for total proteins in this work showed R² and RPD values higher than those observed by Ritthiruangdej and co-workers (R² = 0.93; RPD = 3.41) in the prediction of chemical composition of pork sausages by NIRS^[33] and by Hermida and co-workers (R² = 0.94; RPD = 4.05) in analysis silage total proteins.^[41] Finally, the results obtained for fats showed a much higher R² and RPD values than those obtained by Salguero-Chaparro and co-workers (R² = 0.79; RPD = 2.37)^[21] and by Cayuela and Pérez-Camino (R² = 0.80; RPD = 2.67).^[43] The highly predictive performances for moisture, proteins, and fats from our study might be probably benefited by milling walnut kernel to obtain homogeneous samples. Previous studies also demonstrated that grounding treatments greatly promote the prediction ability of NIRS.^[32,44] The grounding treatment does not allow the application of NIRS as a non-destructive and on-line analytical technique. Nevertheless, it seems necessary to ensure accurate prediction results.

Conclusions

In the current trial, the capability of NIRS to predict the chemical compositions in grounding walnut kernel has been evaluated. Results have demonstrated that NIRS technology has excellent predictive performance for some chemical parameters, such as moisture, fats, and proteins, and can be employed to analyze these components in the walnut kernel, as an alternative to traditionally chemical methods. In view of the spectral information that it provides, NIRS technology could be very useful for the classification of walnut based on some qualitative aspects, such as fats, proteins, and moisture, which is crucial to walnut producers when they purchase walnut from their suppliers and determine the final destination of the walnut.

Declaration of conflict

The authors have declared no conflict of interest

Funding

This study was supported by National Natural Science Foundation of China (Grant No. 31501443) and the Agricultural Research Project of Shaanxi Province Science and Technology Department (Grant No. 2014K01-10-04).

Additional information

Funding

This study was supported by National Natural Science Foundation of China (Grant No. 31501443) and the Agricultural Research Project of Shaanxi Province Science and Technology Department (Grant No. 2014K01-10-04).

Appendix

Appendix A. Effects of math treatments on the calibration equation parameters.

		Calibration equation parameters
Components	Mathematical pretreatments	SEC	Rc^Citation2	SECV	1-VR
Moisture	0,4,4,1	0.057	0.959	0.077	0.912
	1,4,4,1	0.055	0.962	0.074	0.912
	2,4,4,1	0.059	0.956	0.076	0.935
	3,4,4,1	0.057	0.959	0.079	0.930
	4,4,4,1	0.054	0.962	0.071	0.936
Proteins	0,4,4,1	0.235	0.955	0.268	0.942
	1,4,4,1	0.223	0.959	0.247	0.950
	2,4,4,1	0.191	0.967	0.222	0.956
	3,4,4,1	0.207	0.961	0.252	0.943
	4,4,4,1	0.225	0.955	0.271	0.934
Fats	0,4,4,1	0.534	0.891	0.593	0.878
	1,4,4,1	0.531	0.892	0.591	0.876
	2,4,4,1	0.531	0.892	0.594	0.867
	3,4,4,1	0.575	0.874	0.599	0.865
	4,4,4,1	0.431	0.929	0.590	0.889

The regression method and scattering method were modified partial least square loading (MPLS) and standard normal variate + detrending (SNV+DT), respectively.

SEC: standard error of calibration; R_c²: coefficient of determination of calibration; SECV: standard error of the internal cross-validation; 1-VR: coefficient of determination of the internal cross-validation.

Appendix B. Effects of scattering treatments on the calibration equation parameters.

		Calibration equation parameters
Components	Scattering pretreatments	SEC	Rc^Citation2	SECV	1-VR
Moisture	None	0.053	0.965	0.071	0.935
	SNV+DT	0.054	0.962	0.071	0.936
	SNV	0.054	0.962	0.072	0.931
	DT	0.052	0.965	0.071	0.936
	SMSC	0.055	0.962	0.073	0.949
	W MSC	0.055	0.961	0.072	0.948
	IMSC	0.054	0.963	0.071	0.933
Proteins	None	0.216	0.958	0.236	0.950
	SNV+DT	0.191	0.967	0.222	0.956
	SNV	0.206	0.961	0.232	0.952
	DT	0.216	0.959	0.243	0.948
	SMSC	0.197	0.965	0.226	0.954
	W MSC	0.209	0.960	0.228	0.954
	IMSC	0.194	0.967	0.231	0.953
Fats	None	0.226	0.959	0.302	0.914
	SNV+DT	0.431	0.929	0.590	0.889
	SNV	0.171	0.979	0.291	0.936
	DT	0.226	0.959	0.302	0.914
	SMSC	0.171	0.979	0.291	0.937
	W MSC	0.285	0.949	0.345	0.902
	IMSC	0.172	0.979	0.291	0.936

The regression method was MPLS. And the mathematical pretreatments were “4,4,4,1,” “2,4,4,1,” and “4,4,4,1” for moisture, proteins and fats, respectively.

Appendix C. Effects of regression methods on the calibration equation parameters.

		Calibration equation parameters
Components	Regression methods	SEC	Rc^2	SECV	1-VR
Moisture	Modified PLS	0.052	0.965	0.071	0.936
	PLS	0.056	0.961	0.071	0.935
	PCA	0.077	0.924	0.085	0.908
Proteins	Modified PLS	0.191	0.967	0.222	0.956
	PLS	0.231	0.950	0.260	0.938
	PCA	0.236	0.949	0.250	0.943
Fats	Modified PLS	0.171	0.979	0.291	0.937
	PLS	0.227	0.968	0.298	0.923
	PCA	0.212	0.969	0.294	0.927

The scattering method were DT, SNV+DT, and SMSC for moisture, proteins and fats, respectively.

The mathematical pretreatments were “4,4,4,1,” “2,4,4,1,” and “4,4,4,1” for these compositions above, respectively.