Development of Attenuated Total Reflectance Mid-Infrared (ATR-MIR) and Near-Infrared (NIR) Spectroscopy for the Determination of Resistant Starch Content in Wheat Grains

The chemical method for the determination of the resistant starch (RS) content in grains is time-consuming and labor intensive. Near-infrared (NIR) and attenuated total reflectance mid-infrared (ATR-MIR) spectroscopy are rapid and nondestructive analytical techniques for determining grain quality. This study was the first report to establish and compare these two spectroscopic techniques for determining the RS content in wheat grains. Calibration models with four preprocessing techniques based on the partial least squares (PLS) algorithm were built. In the NIR technique, the mean normalization + Savitzky–Golay smoothing (MN + SGS) preprocessing technique had a higher coefficient of determination (Rc2 = 0.672; Rp2 = 0.552) and a relative lower root mean square error value (RMSEC = 0.385; RMSEP = 0.459). In the ATR-MIR technique, the baseline preprocessing method exhibited a better performance regarding to the values of coefficient of determination (Rc2 = 0.927; Rp2 = 0.828) and mean square error value (RMSEC = 0.153; RMSEP = 0.284). The validation of the developed best NIR and ATR-MIR calibration models showed that the ATR-MIR best calibration model has a better RS prediction ability than the NIR best calibration model. Two high grain RS content wheat mutants were screened out by the ATR-MIR best calibration model from the wheat mutant library. There was no significant difference between the predicted values and chemical measured values in the two high RS content mutants. It proved that the ATR-MIR model can be a perfect substitute in RS measuring. All the results indicated that the ATR-MIR spectroscopy with improved screening efficiency can be used as a fast, rapid, and nondestructive method in high grain RS content wheat breeding.


Introduction
Resistant starch (RS) is the starch that cannot be converted into glucose when passing through the healthy small intestine [1]. Owing to its indigestion, RS can increase satiety and reduce calorie intake, which could reduce postprandial blood glucose levels [2], regulate the intestinal metabolism [3], reduce colon cancer risk [4], control bodyweight [5], and absorb minerals [6]. Due to its benefits to human health, the RS studies have attracted considerable attentions and promoted to be one of the important discoveries about the relationship between carbohydrates and human health [1,2,7]. It is also becoming a hot topic for function food breeding [7][8][9].
Starch in grains is the major source of carbohydrates in the human diet. e improvement of the RS content in grains is an important goal for breeding. A few high RS content grain varieties, such as RS111 [10], the hulless barley variety Himalaya 292 [11], and durum wheat [12], have been released to the public. While, they still cannot satisfy the growing demand. Induced mutagenesis and selection of natural mutations are still the major approaches for the breeding of high RS varieties [7,[13][14][15][16][17]. Currently, enzyme hydrolysis and the chromogenic method are the commonly used methods for RS measurement. ese methods are destructive, time-consuming, costly, and cumbersome, thus delaying the process of research in RS [18]. A simple, fast, and nondestructive method for screening mutants with millions of mutants becomes vital for breeding.
Infrared spectroscopy, including near-infrared (NIR, 950-1650 nm) and attenuated total reflectance mid-infrared (ATR-MIR, 525-4000 cm −1 ) spectroscopy, has been widely used as a simple, fast, and reliable substitute to conventional methods in discrimination of chemical composition [19][20][21]. MIR spectra can identify the fundamental vibrational absorption of functional groups in the mid-infrared region (525-4000 cm −1 ), while the NIR spectra in the range of 950 nm and 1650 nm identify overtone information and combinations of these vibrations [19][20][21]. e NIR technique was adopted as an official method for the prediction of crude proteins in wheat grains by the American Association of Cereal Chemists (AACC) [22]. A model based on NIR spectroscopy has been expanded to the investigation of the amylose content [23], lipid content [24], water content [25], and deoxynivalenol content in durum wheat [26] and for monitoring the wheat gluten enzyme [27]. Meanwhile, the MIR spectroscopy models have been used to analyze sugars in barley [19,28], ash and moisture content in soybean [24], and proteins and lipids in different wheat varieties [29], as well as to perform nitrate determination in paddy soil [30].
Wheat (Triticum aestivum L.) feeds about 40% of the world's population [31]. Wheat grains with the high RS content could provide additional health benefits [8,9,31]. While, there is no spectroscopy model for determining the content of RS in wheat grains yet. Furthermore, mutation breeding is the major approach for the breeding of high RS varieties [13][14][15][16][17]. However, there is still no simple, fast, and nondestructive method for screening high RS mutants in breeding.
e aims of this study are to develop a simple, fast, nondestructive method for the determination of the RS content in wheat grains by using NIR and ATR-MIR spectroscopy and to apply the developed spectral methods to screen the high grain RS content wheat mutants.

Samples.
Based on the rule of the range of the RS content in the calibration set should cover those in the validation set, a total of sixty-four (n � 64) wheat accessions were randomly divided into a calibration set (fifty-one wheat samples, Table 1) and a validation set (thirteen wheat samples, Table 2) in a ratio of 4 : 1. e calibration set and the validation set were used to develop and validate the best calibration model for the prediction of the RS content in wheat grains by NIR and ATR-MIR approaches. e M 5 generation wheat mutant library, including 1010 mutant lines, was used to screen the high RS content mutant lines with the best calibration model from the above results. e mutants originated from wheat accession YUW-1-207 and were irradiated by a 50 Gy 7 Li ion beam.
All wheat materials were grown at Yangtze University field stations in 2017-2018. e field trial experiments were arranged randomized with three replications for each accession. Each replicate was designed 1.2 m long and 0.85 m wide. e seeding density was kept 30 per row. Fertilization, pest, and disease control were performed on a regular basis.
e analysis was only based on the plants in the middle row.

Chemical Measured RS Content.
Whole grain flour was prepared from each sample by grinding in a pulveriser (Perten Laboratory Mill 3100), which was fitted with a 0.8 mm screen. e RS content (the amount of RS as a percentage of whole grain) was measured for 100 mg whole grain flour using a resistant starch assay kit (K-RSTAR, Megazyme Co., Wicklow, Ireland) following the manufacturer's instructions. e sample was treated with 10 mg/mL pancreatic a-amylase and 3 U/mL amyloglucosidase (AMG) enzymes for hydrolysis and solubilization of nonresistant starch. After the enzymatic reaction was terminated by adding a 50% ethanol solution, resistant starch was recovered as a pellet by centrifugation (approx. 4000 r/min, 10 min). Resistant starch in the pellet was dissolved in 2 M KOH before the reacted solution was repeatedly washed and decanted. en, starch in the solution was quantitatively hydrolyzed to glucose with AMG. D-glucose was measured with glucose oxidase/peroxidase (GOPOD) reagent at 510 nm wavelength against the reagent blank [29]. All samples were measured with three replicates. e standard RS sample from the reagent kit was used as a control in each round of reactions.

NIR and ATR-MIR Spectroscopy.
NIR spectra were collected in the range between 950 nm and 1650 nm using a DA7200 spectrometer (Perten Instruments Inc., Sweden). In the NIR method, approximately 4 g of wheat grains per sample was scanned in triplicate in a small ring cup. Each spectrum represented the average of 32 scans and was recorded as log (1/R) at 2 nm increments.
MIR spectra were scanned based on a Nicolet iS5 Fourier transform infrared spectrometer ( ermoFisher Scientific, USA) with the iD7 attenuated total reflectance (ATR) accessory. e ATR-MIR spectra of each sample were obtained by taking the means of 16 scans at a resolution of 4 cm −1 , in the range between 525 and 4000 cm −1 , with a background of 16 scans. e air was recorded as a reference background spectrum. e ATR crystal was carefully cleaned with ethanol after each sample measurement.

Data Analysis.
e NIR and ATR-MIR spectra were uploaded to Unscrambler 9.7 software (CAMO Corporation, USA) for chemometric analysis. e models for the calibration between the measured values and the infrared spectra were established using partial least squares (PLS) regression with full cross-validation. e quality of the models was assessed by the determination coefficient of calibration (R 2 c ), the determination coefficient of prediction (R 2 p ), the root mean square error of calibration (RMSEC), and the root mean square error of prediction (RMSEP) [32]. Moreover, the residual predictive deviation (RPD), a statistical parameter defined as the ratio of the standard deviation (SD) to the RMSEP [30,32], was used to assess the predictive ability of the calibration models. e preprocessing methods were used to eliminate the interferences of background signal, random noise, and light scattering from the spectra, which can be divided into the scatter correction group and spectral derivatization group [33,34]. Four preprocessing methods including Gaussian filter smoothing (GFS), multiplicative scatter correction (MSC), baseline mean normalization (MN), and Savitzky-Golay smoothing (SGS) were used to transform the NIR and ATR-MIR spectra before calibration to eliminate interference noise such as baseline drift, tilt and reverse, and light scattering [35,36]. e MN, baseline, and MSC preprocessing methods belonged to the scatter correction group, and the SGS and GFS belong to the spectral derivatization group. e developed model, with the highest value of determination coefficient, the lowest value of root mean square error (RMSE), and the highest value of RPD, was chosen as the best calibration model.

Chemical Measured Wheat Grain RS Content.
e RS content in whole wheat grain flour samples was measured by the AOAC method in the calibration set ranged from 0.220% to 3.348% with the mean content 1.011% (Table 1); while the RS content in the validation set ranged from 0.267% to 2.842% with the mean content 1.285% (Table 2). e standard deviation (SD) in the calibration set and validation set is 0.679 and 0.697, respectively. e coefficient of variation (CV) in the calibration set and validation set is 67.086% and 53.846%, respectively. A wide distribution of the wheat grain RS content was observed in the calibration set and validation set.

Development of NIR and ART-MIR Prediction Models.
e ATR-MIR spectrum (525-4000 cm −1 ) (Figure 1(a)) region has strong absorption peaks and belongs to the fundamental molecular vibration modes. e peaks between 3600 and 3000 cm −1 were assigned to hydrogen bonded water (O-H stretching vibration). e weak band detected at 1652 cm −1 was responsible for C�O vibration of the decarboxylated groups. e region between 1200 and 950 cm −1 was attributed to the O-C stretch vibrations of the glucose ring [37,38].  For the NIR spectra (950-1650 nm) (Figure 1(b)), two peaks were observed; the weak intensity was found round 1200 nm, and the intense peak was found around 1500 nm. e absorption around 1215 nm and 1483 nm was, respectively, reported to be related to the stretching of C-H and N-H [41]. e collected ATR-MIR and NIR spectra from the calibration set were used to develop calibration models through PLS regression with full cross-validation. For the NIR spectra, the model with MN + SGS preprocessing had the highest R 2 c and RPD, which were 0.672 and 1.464, respectively. At the same time, RMSE in this model reached as low as 0.459 (Table 3). e MN + SGS preprocessing was chosen as the best calibration model for the NIR spectroscopy. For the ATR-MIR spectra, the baseline preprocessing method was chosen as the best calibration model with R 2 c , RPD, and RMSE 0.937, 2.391, and 0.284, respectively (Table 3).
Meanwhile, a much better correlation of the chemical determined values and the predicted values was observed in the ATR-MIR spectroscopy model (Figure 2(a)) than in the NIR spectroscopy model (Figure 2(b)). e correlation between predicted values and real values in the ATR-MIR model was 0.937, while the value in the NIR model was 0.672. e results showed the ATR-MIR spectroscopy model may have a better performance in the prediction of the RS content in wheat grains than the NIR spectroscopy model.

Validation of NIR and ATR-MIR Best Calibration Models.
To verify the accuracy and repeatability of models with the best preprocessing techniques in two spectroscopies, validation was performed in the validation set. e experimentally determined RS values, the predicted RS values, and the calculated relative error (measurement/prediction value) are given in Table 2. For the NIR calibration model with the MN + SGS preprocessing, the relative error ranged from 17.143% to 121.348%, with a mean relative error of 34.028%. For the ATR-MIR calibration model with the baseline preprocessing method, the relative error ranged from 2.931% to 31.220%, with a mean relative error of 15.832%. A linear regression analysis was performed between the measured value and the value predicted by the ATR-MIR model (R 2 � 0.919) (Figure 3(a)) and the NIR model (R 2 � 0.773) (Figure 3(b)).
e results confirmed that the ATR-MIR spectroscopy provided a better performance than NIR spectroscopy for the prediction of the RS content in wheat grains.

Screen of High RS Wheat Mutants by the ATR-MIR Model.
To check the application of the developed ATR-MIT model, the best ATR-MIR calibration model with baseline preprocessing was promoted to predict the grain RS content of 1010 wheat mutants (Figure 4). e predicted RS content of 1010 wheat mutants ranged from 0.101 ± 0.018% to 2.553 ± 0.311%.
Two lines with YUW-RSH1 (2.553 ± 0.311%) and YUW-RSH2 (2.116 ± 0.230%) highest RS content were identified (Table 4). At the same time, the RS content was also validated by the chemical method. e chemical determined RS content in YUW-RSH1 and YUW-RSH2 was 2.572 ± 0.090% and 2.126 ± 0.071%, respectively (Table 4). ere was no significant difference between the predicted values and chemical determined values. e results showed that ATR-MIR spectroscopy can be an effective way to screen and identify high grain RS content materials for wheat breeding.

ATR-MIR Spectroscopy Had a Better Performance for the Prediction of the RS Content in Wheat Grains than NIR
Spectroscopy. e MIR and NIR spectroscopies have become the fastest growing and most compelling modern quantitative analysis technologies for assessment of the quality of both macronutrients and minor compounds in agricultural products and food [21,27,42,43]. In agriculture, protein, tannins, lipids, phytic acid, and most of the amino acids were the commonly detected components in crops [23,[27][28][29]34]. In this study, a total of 64 wheat samples were used to develop the ATR-MIR and NIR   calibration models for the prediction of the RS content in wheat grains. Compared with previous studies, the samples and the distribution of the wheat grain RS content in this study have reached the requirements of infrared basic modeling [20,24,42]. It is commonly accepted that regression models with R 2 above 0.91 are regarded as excellent, R 2 � 0.82-0.9 indicated good predictive ability; R 2 � 0.50-0.65 reveals approximate quantitative performance [44]; and when RPD was greater than 2, the models were considered excellent, whereas values lower than 1.5 indicate not enough for applications [45]. In our results, the best NIR models showed an approximate quantitative performance (R 2 c � 0.672; R 2 p � 0.552; RPD � 1.464), and the best MIR model gave a good prediction performance (R 2 c � 0.927; R 2 p � 0.828; RPD � 2.366). Overall, the ATR-MIR displayed a better performance for the evaluation of the wheat grain RS content.
Previous reports comparing the ATR-MIR and NIR techniques for the measurement of chemical differences and quantitative analysis of substances showed that the NIR and MIR techniques have different prediction effects [21,24,42,46]. In soybean samples, the NIR technique was suggested for the prediction of protein and lipid determination, while the MIR technique was suggested for ash and moisture determination [24]. In rice samples, the NIR technique and the MIR technique were the best predictors of starch and protein, respectively [46]. In this study, we found that the ATR-MIR spectroscopy had a better performance for the prediction of the RS content in wheat grains than NIR spectroscopy based on the PLS regression. Nowadays, the recent developed deep learning and artificial intelligence algorithm could be used to improve the stability and robustness of the spectral model, which may also become trend of the future research [47].

Rapid RS Content Assessment Method for Wheat
Breeding. To date, mutation breeding is the major approach for the breeding of high RS varieties [7,[13][14][15][16][17]. Rapid quality assessment methods are taking an increasing important role in breeding programs; this is especially the case in mutation breeding. In this study, the developed best calibration ATR-MIR model was used to screen out 2 high resistance starch wheat mutants from the mutant library. Comparing with the chemical methods with 24 hours' time cost for each sample, the total time for the spectroscopic method just needs less than five minutes per sample, which provided the possibility to screening tens of thousands of breeding materials. e spectroscopic method could greatly improve the measurement efficiency and provide a new approach for crop resistant starch breeding and research.

Conclusion
MIR and NIR techniques were demonstrated to be useful for the prediction of the RS content in wheat grains. e ATR-MIR technique provided a better predictive ability than the NIR technique. e total time required for the measurement of each sample was less than 5 minutes, compared to approximately 20 hours required for the determination of the RS content by chemical methods. In addition, we confirmed that the use of ATR-MIR spectroscopy to assist in the screening and identification of the wheat RS content was an effective approach.

Data Availability
Majority of the data used to support the findings of this study are included within the article. Other data are made available from the first author and corresponding authors upon request.

Conflicts of Interest
e authors declare that there are no conflicts of interest.

Authors' Contributions
YX and LL conceived and designed the experiments. XW, RW, and LZ performed the experiments, XW, YX, RW, HW, WZ, CZ, and BW performed data analysis, YX, XW, RW, and LL wrote the manuscript. All authors read and approved the manuscript. Rong Wang and Xia Wei contributed equally to this work.