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Article

At-line Prediction of Gelatinized Starch and Fiber Fractions in Extruded Dry Dog Food Using Different Near-Infrared Spectroscopy Technologies

by
Arianna Goi
1,
Carmen L. Manuelian
1,*,
Federico Righi
2 and
Massimo De Marchi
1
1
Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova, Viale dell’Università 16, 35020 Legnaro (PD), Italy
2
Department of Veterinary Science, University of Parma, Via del Taglio 10, 43126 Parma, Italy
*
Author to whom correspondence should be addressed.
Animals 2020, 10(5), 862; https://doi.org/10.3390/ani10050862
Submission received: 14 April 2020 / Revised: 30 April 2020 / Accepted: 14 May 2020 / Published: 16 May 2020
(This article belongs to the Section Companion Animals)
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Abstract

:

Simple Summary

Starch is a non-fibrous carbohydrate that represents an important percentage of pet food composition. The degree of its gelatinization, due to the cooking process, can be a useful indicator of starch digestibility in the diet. Moreover, fiber fractions are important for animals’ health and nutritional status, so pet food industry is interested in the development of an easy and cost-effective method to measure these parameters. Results of this study revealed the applicability of visible/near-infrared spectroscopy to predict total and gelatinized starch, neutral detergent fiber, acid detergent fiber, and acid detergent lignin in pet food. On the other hand, near-infrared transmittance technology showed a scarce accuracy. The developed prediction models for total and gelatinized starch and fiber fractions using visible/near-infrared spectroscopy could be applied during the manufacturing process to perform quality controls.

Abstract

This study aimed to assess the feasibility of visible/near-infrared reflectance (Vis-NIR) and near-infrared transmittance (NIT) spectroscopy to predict total and gelatinized starch and fiber fractions in extruded dry dog food. Reference laboratory analyses were performed on 81 samples, and the spectrum of each ground sample was obtained through Vis-NIR and NIT spectrometers. Prediction equations for each instrument were developed by modified partial least squares regressions and validated by cross- (CrV) and external validation (ExV) procedures. All studied traits were better predicted by Vis-NIR than NIT spectroscopy. With Vis-NIR, excellent prediction models were obtained for total starch (residual predictive deviation; RPDCrV = 6.33; RPDExV = 4.43), gelatinized starch (RPDCrV = 4.62; RPDExV = 4.36), neutral detergent fiber (NDF; RPDCrV = 3.93; RPDExV = 4.31), and acid detergent fiber (ADF; RPDCrV = 5.80; RPDExV = 5.67). With NIT, RPDCrV ranged from 1.75 (ADF) to 2.61 (acid detergent lignin, ADL) and RPDExV from 1.71 (ADL) to 2.16 (total starch). In conclusion, results of the present study demonstrated the feasibility of at-line Vis-NIR spectroscopy in predicting total and gelatinized starch, NDF, and ADF, with lower accuracy for ADL, whereas results do not support the applicability of NIT spectroscopy to predict those traits.

1. Introduction

Starch is a polymer organized in concentric semi-crystalline or amorphous layers constituted by amylose and amylopectin [1]. In companion animals food, starch represents up to 50% of the product [2]. Starch generally derives from one or few cereal grains added up to 60% of the ingredients [3,4], and it represents an important source of energy since its digestion releases molecules of glucose. On the other hand, fibrous fractions, such as neutral detergent fiber (NDF), acid detergent fiber (ADF), and acid detergent lignin (ADL), are physiologically important because of their influence on the ingesta transit time, gastric emptying, and fecal volume [5].
Most of the uncooked cereal starch presents a low digestibility in dogs [4]. Therefore, heat treatments applied during manufacture play a fundamental role in improving the digestibility of starch in food. Due to the high proportion of starch in extruded and baked dog diets, the digestibility of these typologies of dog foods is highly related to the digestibility of this component [4], which can also potentially affects the apparent total tract macronutrient digestibility, fecal characteristics, and fecal fermentative end-products in dogs [6]. Extrusion is a widespread technology to produce commercial dry dog food because of its economicity [7] and its capacity to mix, cook, sterilize, and texturize food ingredients [2]. Extrusion modifies starch structure, leading to the release of amylose and amylopectin, which increases the solubility and the capacity to absorb water of this polysaccharide, reducing at the same time its viscosity [8,9]. The complex of these modifications is known as “gelatinization” [10,11].
Extrusion reduces apparent nitrogen digestibility, enhances colonic fermentation—as revealed by a decrease in fecal pH associated to the assumption of extruded food [12]—slightly increases dry and organic matter digestibility likely due to the increase in starch digestibility, and increases ether extract digestibility [13]. From a quality control perspective, the industry is interested in the ratio between gelatinized and total starch, which indirectly expresses its availability. No information is available in literature about the optimal value of this parameter, but as the ratio expresses the amount of digestible starch among the overall quantity, a high value is usually preferable. Sometimes, a small percentage of resistant starch is guaranteed to assure butyrate production due to its beneficial effects on the gut health [14]. Thus, some companies are claiming proportions of gelatinized starch between 90% and 95% in their food products.
Due to the importance of both fibrous fractions and non-fibrous carbohydrates for the animals’ health and nutritional status, a further interest of the pet food industry is the development of an on-line or at-line rapid and cost-effective method to measure these parameters in order to monitor food composition. The common reference analyses to quantify starch, gelatinized starch, and fiber fractions (NDF, ADF, and ADL) are time-consuming, expensive, and need qualified analysts to be performed. On the other hand, near-infrared spectroscopy is already routinely used in most of pet food factories for the determination of gross composition of their products. This method offers a rapid, objective, easy to manage, chemical-free, and non-destructive analysis of several traits simultaneously at a lower cost than the common reference analyses [15,16,17], facilitating quality control.
Infrared spectroscopy capacity to predict food compounds is based on the bonds between hydrogen and elements like C, O, and N (C-H, O-H, and N-H), making total starch, gelatinized starch, and fibrous fractions good candidates to be accurately predicted with this technology. Infrared spectroscopy has already demonstrated its ability to predict proximate composition parameters—such as moisture, protein, and fat—of dog food [18,19,20], starch, and ADL in compound feeds for rabbits [21] and starch, NDF, ADF, and ADL in compound feeds for swine [22]. However, to the best of our knowledge, there is no information regarding the ability of near-infrared spectroscopy to predict starch, gelatinized starch, and fiber fractions in dog food. Therefore, the aim of this study was to assess the feasibility of visible/near-infrared spectroscopy in reflectance mode and near-infrared spectroscopy in transmittance mode to predict total starch, gelatinized starch, and fiber fractions in extruded dry dog food.

2. Materials and Methods

2.1. Sample Selection

A total of 81 sealed commercial packages of 2, 2.5, and 3 kg of extruded dog food intended for puppies and adults of several breed sizes (Table 1) were collected from March 2018 to April 2019 from a pet food industry in North Italy (Dorado S.r.l.; Monsole di Cona, Venice, Italy). The diversity of the selected products was considered as representative of the availability of extruded dry dog food in the Italian market and provides a high variability that is necessary to develop a prediction model [23,24]. Table 2 shows the chemical composition—dry matter (DM), crude protein (CP), ether extract (EE), and ash—as reported by the manufacturer. Moreover, Tables S1 and S2 report the ingredients and chemical composition, respectively, of each single package included in the study. Once received, packages were stored in absence of light and at room temperature in a dedicated area of the food laboratory of the Department of Agronomy, Food, Natural resources, Animals and Environment of the University of Padova (Legnaro, Italy) until analyses were performed. For the analyses, kibbles (100 g) from each package were grounded in a mill with a 1-mm screen (Retsch Grindomix GM200; Retsch GmbH & Co, Haan, Germany) and divided into two aliquots: one was subjected to chemical analysis, and the other underwent spectrophotometric analysis with two different instruments.

2.2. Reference Analysis

Milled samples were analyzed for the total amount of starch using an internal method. Briefly, 500 mg of sample were weighed in a 100-mL PYREX glass tube (SciLabware, Stoke on Trent, the United Kingdom) and added 50 mL of KOH (Carlo Erba, Milano, Italy) 0.5 M; the mixture was quickly mixed with an electric vortex mixer, heated in an shaking water bath oscillating 80 times/min at 60 °C for 60 min, and then cooled to room temperature. Thereafter, glacial acetic acid of a high degree of purity (>95%) (Carlo Erba, Milano, Italy) was added by pipetting to the solution until reaching a pH between 4.6 and 4.8, measured by pH-meter with temperature probe correction. Subsequently, amyloglucosidase solution was produced by adding in a 50-mL PYREX glass flask (Schott Duran, Wertheim, Germany) 22.5 mg of amyloglucosidase standard from Aspergillus niger (10115, Sigma-Aldrich, Steinheim, Gemany) with an aliquot of deionized water (Millipore Corporation, Burlington, MA, USA), dissolved in an ultrasonic bath for 10 min, and made up to volume with deionized water. Two milliliters of the amyloglucosidase solution were added to the sample solution and incubated over night at 40 °C for the enzymatic hydrolysis. After cooling, the whole solution was transferred to a 100-mL glass flask (Schott Duran, Wertheim, Germany), diluted with deionized water to capacity, and shaken. Then, an aliquot of 10 mL was transferred into a 10-mL PYREX glass tube (SciLabware, Stoke on Trent, the United Kingdom), centrifuged at 2700× g for 10 min at room temperature (20 °C), and the supernatant was filtered through a 0.45-µm filter. Finally, 10 µL of the filtered supernatant was injected in a high-performance liquid chromatography spectra system equipped with an Aminex HPX 87H column (Bio-Rad, Hercules, CA, USA) and aqueous solution of sulfuric acid 0.0025 N as the mobile phase, for the quantification of glucose. The working conditions were: flow rate of 0.6 mL/min and cell internal temperature of 38 °C. A glucose standard solution and a calibration line were made for the quantitative determination of glucose. To prepare the glucose standard solution, 200 mg of D-(+)-glucose anhydrous (G-7528, Sigma-Aldrich, Steinheim, Germany) were weighed in a 50-mL glass flask and were made up to volume with deionized water. Afterwards, for each calibration level, a known aliquot of the glucose standard solution was put in a 25-mL flask making up to volume with sulfuric acid 0.1 N. Starch quantification was performed in duplicate.
Damaged starch quantification was performed using a Megazyme kit (Megazyme Intl. Ireland Ltd., Co., Wicklow, Ireland) following the method 76-31.01 of the American Association of Cereal Chemists (AACC) that consists in an enzyme digestion procedure with purified fulgal α-amylase that only affects damaged starch. The oligosaccharides formed were treated with purified amyloglucosidase to obtain the glucose. Even if quantified damaged starch does not completely correspond to gelatinized starch, considering the extrusion process conditions, we assume that all the damaged starch in the matrix undergo gelatinization, and thus, in this case, the two parameters present comparable values [13,25]. The fiber fractions were also analyzed according to official methods [26]: NDF, method 2002.04, and ADF and ADL, method 973.18.

2.3. Near-Infrared Spectroscopy Analysis

Each sample was analyzed using two infrared instruments: a visible/near-infrared spectrophotometer in reflectance mode (Vis-NIR) and a near-infrared spectrophotometer in transmittance mode (NIT). Both instruments worked at room temperature (20 °C) and were located in the same laboratory. The same sample was scanned consecutively with both instruments. Spectra were collected using ISIscan Nova and Mosaic software (FOSS Electric A/S, Hillerød, Denmark).
For the Vis-NIR instrument, 50 g of ground sample were placed in a large FOSS cup (diameter 105 mm, depth 35 mm; FOSS Electric A/S, Hillerød, Denmark) and scanned by NIRS DS2500 (FOSS Electric A/S, Hillerød, Denmark) from 400 to 2500 nm every 0.5 nm. Each spectrum was the average of 32 subspectra collected during the automatic rotation of the FOSS cup and recorded as log(1/reflectance). For the NIT instrument, the same 50-g ground sample was placed in a sample cup (diameter 140 mm, depth 14 mm) and scanned by FoodScan (FOSS Electric A/S, Hillerød, Denmark) from 850 to 1050 nm every 2 nm. Each spectrum was the average of 16 subspectra collected during the automatic rotation of the cup and recorded as log (1/transmittance).

2.4. Chemometric Analysis

Chemometric analysis was performed using WinISI 4 software (Infrasoft International, Port Matilda, PA, USA) through modified partial least square regression analysis. The prediction equations for each trait were developed using (i) the complete dataset (n = 81) and then tested by leave-one-out cross-validation, and (ii) a subset as the calibration set using 75% of the samples (n = 60) and then tested on the remaining 25% of the samples performing an external validation (n = 21). To establish the calibration and the validation set, the complete dataset was randomly divided into two subsets with similar mean and standard deviations for each trait. In the calibration procedure, to increase the accuracy in terms of the coefficient of determination and residual predictive deviation, three passes of outliers elimination were applied. The critical T-statistic value set for T-outliers detection was 2.5, removing the samples whose predicted value deviated more than 2.5 standard errors of cross-validation from the reference value. In order to develop the most accurate calibration models, different combinations of scatter corrections (NONE, no correction; D, detrending; SNV, standard normal variate; SNV + D, standard normal variate and detrending; MSC, multiplicative scatter correction; WMSC, weighted multiplicative scatter correction; and ISC, inverted scatter correction) and mathematical treatments (0,0,1,1; 1,4,4,1; 1,8,8,1; 2,5,5,1; and 2,10,10,1, where the first digit is the number of the derivative, the second is the gap over which the derivative is calculated, the third is the number of data points in the first smoothing, and the fourth is the number of data points in the second smoothing [27]) were applied.
The best calibrations were assessed based on the number of latent factors (LF), the coefficient of determination of calibration (R2C), cross-validation (R2CrV), and external validation (R2ExV); the standard error of calibration (SEC), cross-validation (SECrV), and external validation (SEP); and the residual predictive deviation of cross-validation (RPDCrV) and of external validation (RPDExV), calculated as the ratio of SD to SECrV or SEP, respectively [28]. Residuals of prediction equations obtained performing cross- and external validations were normally distributed, and bias did not differ statistically from zero.

3. Results

3.1. Chemical Composition of the Samples

Total and gelatinized starch were 34.22 g/100 g and 22.99 g/100 g on DM, respectively (Table 3). The average ratio gelatinized/total starch was 0.68. Fiber fractions were 17.48, 4.67, and 1.85 g/100 g DM for NDF, ADF, and ADL, respectively (Table 3). Variation of starch and gelatinized starch among samples was around 25%, while coefficient of variation (CV) of the fiber fractions ranged from 37% (NDF) to 57% (ADF).

3.2. Near-Infrared Spectrum and Predictions Models

The average raw absorbance spectrum of the samples obtained with the two instruments are depicted in Figure 1. The Vis-NIR raw spectrum revealed sharp peaks at 465, 663, 1202, 1570, 1726, 1755, 1935, 2307, and 2348 nm and two wide peaks from 1454 to 1495 nm and from 2060 to 2168 nm (Figure 1a). On the other hand, NIT raw spectrum followed a quite fast-decreasing curve (Figure 1b).
The performance of the best prediction models for each instrument using the complete dataset are reported in Table 4, whereas using subsets for external validation are reported in Table 5. Outliers detected for the complete dataset were ≤9% for all the variables, except for the total starch predicted by Vis-NIR and ADF predicted by NIT (14% and 11% of samples deleted, respectively). When performing external validation (Table 5), outliers detected were ≤10% for all the variables, except for NDF predicted by NIT (15%). With the complete dataset (Table 4), LF ranged from 8 to 10 in both instruments. When performing external validation (Table 5), LF ranged from 7 to 10 for Vis-NIR and from 6 to 10 for NIT.
The best prediction models for Vis-NIR with the complete dataset (Table 4) were obtained when using SNV + D, MSC, WMSC, or ISC as the scatter correction and the first or second derivatives as the mathematical pretreatment. On the other hand, for NIT with the complete dataset (Table 4), the best prediction models were achieved without scatter correction (NONE) or D and with raw spectra or the first derivative. The best prediction models for Vis-NIR performing an external validation (Table 5) were obtained using MSC, WMSC, or ISC preprocessing techniques to reduce the variability due to scatter and using the first and second derivatives as the mathematical pretreatment. On the other hand, for NIT with external validation (Table 5), the best prediction models were obtained with NONE, D, or ISC, with raw spectra, or with the first or second derivatives.
For the complete dataset (Table 4), all prediction models for Vis-NIR achieved an RPDCrV > 3.5, being the best the one for total starch (R2CrV = 0.97; RPDCrV = 6.33) and the worst for ADL (R2CrV = 0.93; RPDCrV = 3.73). On the other hand, considering the complete dataset (Table 4), RPDCrV of the prediction models with NIT ranged from 1.75 (ADF) to 2.61 (ADL). When performing an external validation (Table 5), all prediction models for Vis-NIR achieved an RPDExV > 4, with the exception of ADL, being the best equation the one for ADF (R2ExV = 0.97; RPDExV = 5.67) and the worst for ADL (R2ExV = 0.84; RPDExV = 2.46). On the other hand, in external validation (Table 5), RPDExV of the prediction models obtained with NIT ranged from 1.71 (ADL) to 2.16 (total starch). Residuals of prediction equations obtained performing cross- and external validations were normally distributed, and bias did not differ statistically from zero.

4. Discussion

4.1. Samples Composition

Gross composition (Table 2) of the samples was consistent with the European Pet Food Industry Federation (FEDIAF) [29] recommendations. Moreover, total starch and fiber fractions variability, as well as the ingredients used, reflects the variability of food in the market connected to the formulations performed to meet the requirements of animals of different life stages and sizes.
Total starch content (Table 3) in the present study was consistent with those reported by several authors who determined this parameter in dry dog food using a reference analysis [5,30,31,32]. The ADF content (Table 3) agreed with results from De-Oliveira et al. [5] and Tran et al. [31]. The gelatinized starch content (Table 3) and the ratio gelatinized/total starch were slightly lower than the ones reported by Tran et al. [31]. In particular, the latter author reported a proportion of gelatinized starch of 0.76 in extruded dog food, which may be due to a slightly lower content of fat (9.7 g/100 g DM) in their samples, since fat content during extrusion has an inverse effect on the starch gelatinization degree [33]. However, in dog food, fats are usually added after the product is extruded, and differences on dog food composition (other than fat) and the processing condition may have contributed to the differences observed. Moreover, we obtained a greater NDF content than De-Oliveira et al. [5], who reported an average of 8.5% of NDF (DM basis) with a lower CV. The greater variability obtained in the present study is a consequence of the high variability of samples included, which is a key point to developing robust calibration models [23,24]. The ADL content (Table 3) was greater than the one reported by Opitz et al. [34] in extruded dog food but lower than that reported by Cipollini [35], who quantified ADL in the same matrix.

4.2. Vis-NIR vs. NIT Prediction Models Accuracy

The average raw absorbance spectrum for Vis-NIR (Figure 1a) is consistent with the one reported in other studies with ground dog food samples [23,36,37], even if they did not consider the visible region of the spectrum. The absorption signals observed at 1202 and 2348 nm are related to the presence of carbohydrates [38,39]; signals around 1460 and 1935 nm to water [39,40]; absorption signals at 1490 and 1570 nm to protein [40,41]; at 1726, 1755, and 2308 nm to lipids [17]; and at approximately 1450 and 2100 nm to starch [40]. On the other hand, NIT raw spectrum followed a decreasing curve, and no clear peaks were observed (Figure 1b). However, the NIT spectra agreed with the absorbance observed in the Vis-NIR spectra (Figure 1a) on the wavelength range from 850 to 1050 nm. The feasibility of near-infrared spectroscopy to evaluate pet food general composition has been assessed by several authors [18,19,23,36,37,42], but studies to predict total starch, damaged or gelatinized starch, NDF, ADF, and ADL in pet food are lacking.
Fitting statistics of the best prediction models were similar for cross- (Table 4) and external validations (Table 5) when using the same near-infrared spectroscopy instrument. Prediction models obtained by Vis-NIR technology with cross-validation can be considered good for NDF and ADL (RPD between 3.0 and 4.0) and excellent (RPD > 4.0) for total starch, gelatinized starch, and ADF. In compound feed for swine, a similar R2CrV was obtained for total starch using a near-infrared instrument working between 1100 and 2500 nm [22]. However, we obtained better R2CrV for fiber fractions than the cited authors (0.92, 0.95, and 0.85 for NDF, ADF, and ADL, respectively) [22]. When prediction models for Vis-NIR were tested with an external validation, predictions were considered as excellent for all the traits except for ADL, in which the RPD was below 2.5. In compound feed for rabbits, a lower R2ExV has been reported for total starch, NDF, ADF, and ADL (0.90, 0.50, 0.82, and 0.59, respectively) using a near-infrared instrument that worked between 1100 and 2500 nm [21] in comparison to those obtained in the present study.
On the other hand, prediction models using NIT were, in general, unsatisfactory (RPDCrV or RPDExV < 2.5; Table 4 and Table 5, respectively). Only when using cross-validation, prediction models achieved for total starch and ADL were adequate for screening. The slight difference between the fitting statistics obtained for cross- and external validations affected the interpretation of the model accuracy for some traits, because that interpretation is based on pre-established thresholds [43]. However, those changes were in agreement with the results obtained in the prediction of fatty acids and minerals in cheese when comparing cross- and external validations [44]. Although both procedures (cross- and external validations) are accepted for chemometric analysis, results obtained with external validation better reflects the prediction ability of the model for future samples [45].
Overall, Vis-NIR resulted to be better than NIT to predict total starch, gelatinized starch, and fiber fractions in extruded dry dog food. The better performance of infrared in reflectance than in transmittance mode has been already reported in meat [46] and fresh grass silage quality traits [47]; despite that, this was not observed when predicting minerals in fresh cheese [48]. Considering Figure 1, the greater accuracy of Vis-NIR technology is probably due to the extent of the range of the wavelength that provided a more informative spectrum in the first case rather than in the second one.

5. Conclusions

The results obtained demonstrated that total and gelatinized starch, as well as NDF, ADF, and ADL, can be successfully predicted with Vis-NIR spectroscopy. On the other hand, prediction models when using NIT spectroscopy presented low accuracy, which suggests that this instrument is not adequate to predict starch and fiber fractions in extruded dry dog food. A prediction model to determine gelatinized starch during the manufacture of dry dog food could be useful to monitor its proportion with respect to total starch. Moreover, to the best of our knowledge, this is the first time that near-infrared prediction models have been developed to determine gelatinized starch in pet food. Considering the good prediction models for total starch, gelatinized starch, NDF, ADF, and ADL, further studies are needed to confirm the feasibility of Vis-NIR to perform on-line or at-line quality control.

Supplementary Materials

The following are available online at https://www.mdpi.com/2076-2615/10/5/862/s1: Table S1. List of ingredients for each sample as declared by the manufacturer on the label of the product. Table S2. Chemical composition of each sample as declared by the manufacturer on the label of the product.

Author Contributions

Conceptualization, M.D.M.; data curation, A.G.; formal analysis, A.G.; funding acquisition, M.D.M.; investigation, A.G.; methodology, A.G. and M.D.M.; resources, M.D.M.; software, A.G.; supervision, M.D.M.; validation, C.L.M. and F.R.; visualization, A.G.; writing—original draft, A.G.; and writing—review and editing, C.L.M., F.R., and M.D.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Acknowledgments

The authors gratefully acknowledge Dorado S.r.l. (Monsole di Cona, Venice, Italy) for providing the samples.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Buléon, A.; Colonna, P.; Planchot, V.; Ball, S. Starch granules: Structure and biosynthesis. Int. J. Biol. Macromol. 1998, 23, 85–112. [Google Scholar] [CrossRef] [Green Version]
  2. Tran, Q.D.; Hendrix, W.H.; van der Poel, A.F.B. Effects of extrusion processing on nutrients in dry pet food. J. Sci. Food Agric. 2008, 1926, 1487–1493. [Google Scholar] [CrossRef]
  3. Twomey, L.N.; Pethick, D.W.; Rowe, J.B.; Choct, M.; Pluske, J.R.; Brown, W.; Laviste, M.C. The use of sorghum and corn as alternatives to rice in dog foods. J. Nutr. 2002, 132, 1704S–1705S. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Inal, F.; Alatas, M.S.; Kahraman, O.; Inal, S.; Uludağ, M.; Gürbüz, E.; Polat, E.S. Barley as an alternative to rice in dog food. Turkish J. Vet. Anim. Sci. 2017, 41, 770–774. [Google Scholar] [CrossRef]
  5. De-Oliveira, L.D.; Takakura, F.S.; Kienzle, E.; Brunetto, M.A.; Teshima, E.; Pereira, G.T.; Vasconcellos, R.S.; Carciofi, A.C. Fibre analysis and fibre digestibility in pet foods—A comparison of total dietary fibre, neutral and acid detergent fibre and crude fibre. J. Anim. Physiol. Anim. Nutr. (Berl) 2012, 96, 895–906. [Google Scholar] [CrossRef]
  6. Beloshapka, A.N.; Alexander, L.G.; Buff, P.R.; Swanson, K.S. The effects of feeding resistant starch on apparent total tract macronutrient digestibility, faecal characteristics and faecal fermentative end-products in healthy adult dogs. J. Nutr. Sci. 2014, 3, 1–5. [Google Scholar] [CrossRef] [Green Version]
  7. Gibson, M.; Alavi, S. Pet Food Processing—Understanding Transformations in Starch during Extrusion and Baking. Cereal Foods World 2013, 58, 232–236. [Google Scholar] [CrossRef] [Green Version]
  8. Rokey, G.J. Petfood and fishfood extrusion. In The Technology of Extrusion Cooking; Frame, N.D., Ed.; Springer: Boston, MA, USA, 1994; pp. 144–189. [Google Scholar]
  9. Rosentrater, K.A.; Evers, A.D. Chemical components and nutrition. In Kent’s Technology of Cereals: An Introduction for Students of Food Science and Agriculture; Woodhead Publishing: Cambridge, UK, 2018; pp. 267–368. ISBN 9780081005293. [Google Scholar]
  10. Tester, R.F. Properties of damaged starch granules: Composition and swelling properties of maize, rice, pea and potato starch fractions in water at various temperatures. Food Hydrocoll. 1997, 11, 293–301. [Google Scholar] [CrossRef]
  11. Li, E.; Dhital, S.; Hasjim, J. Effects of grain milling on starch structures and flour/starch properties. Starch/Staerke 2013, 66, 15–27. [Google Scholar] [CrossRef]
  12. Stroucken, W.P.J.; Van Der Poel, A.F.B.; Kappert, H.J.; Beynen, A.C. Extruding vs pelleting of a feed mixture lowers apparent nitrogen digestibility in dogs. J. Sci. Food Agric. 1996, 71, 520–522. [Google Scholar] [CrossRef]
  13. İnal, F.; Alataş, M.S.; Kahraman, O.; İnal, Ş.; Uludağ, M.; Gürbüz, E.; Polat, E.S. Using of Pelleted and Extruded Foods in Dog Feeding. Kafkas Univ. Vet. Fak. Derg. 2018, 24, 131–136. [Google Scholar]
  14. Brouns, F.; Kettlitz, B.; Arrigoni, E. Resistant starch and “the butyrate revolution”. Trends Food Sci. Technol. 2002, 13, 251–261. [Google Scholar] [CrossRef]
  15. De Marchi, M.; Berzaghi, P.; Boukha, A.; Mirisola, M.; Gallo, L. Use of near infrared spectroscopy for assessment of beef quality traits. Ital. J. Anim. Sci. 2007, 6, 421–423. [Google Scholar] [CrossRef]
  16. De Marchi, M.; Manuelian, C.L.; Ton, S.; Cassandro, M.; Penasa, M. Feasibility of near infrared transmittance spectroscopy to predict fatty acid composition of commercial processed meat. J. Sci. Food Agric. 2018, 98, 64–73. [Google Scholar] [CrossRef] [PubMed]
  17. Manley, M. Near-infrared spectroscopy and hyperspectral imaging: Non-destructive analysis of biological materials. Chem. Soc. Rev. 2014, 43, 8200–8214. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  18. Castrillo, C.; Baucells, M.; Vicente, F.; Mun, F.; Andueza, D. Energy evaluation of extruded compound foods for dogs by near-infrared spectroscopy. J. Anim. Physiol. Anim. Nutr. (Berl) 2005, 89, 194–198. [Google Scholar] [CrossRef] [PubMed]
  19. Hervera, M.; Baucells, M.D.; Torre, C.; Buj, A.; Castrillo, C. Prediction of digestible energy value of extruded dog food: Comparison of methods. J. Anim. Physiol. Anim. Nutr. (Berl) 2008, 92, 253–259. [Google Scholar] [CrossRef]
  20. De Marchi, M.; Righi, F.; Meneghesso, M.; Manfrin, D.; Ricci, R. Prediction of chemical composition and peroxide value in unground pet foods by near-infrared spectroscopy. J. Anim. Physiol. Anim. Nutr. (Berl) 2016, 102, 337–342. [Google Scholar] [CrossRef]
  21. Xiccato, G.; Trocino, A.; De Boever, J.L.; Maertens, L.; Carabaño, R.; Pascual, J.J.; Perez, J.M.; Gidenne, T.; Falcao-E-Cunha, L. Prediction of chemical composition, nutritive value and ingredient composition of European compound feeds for rabbits by near infrared reflectance spectroscopy (NIRS). Anim. Feed Sci. Technol. 2003, 104, 153–168. [Google Scholar] [CrossRef]
  22. Aufrère, J.; Graviou, D.; Demarquilly, C.; Perez, J.M.; Andrieu, J. Near infrared reflectance spectroscopy to predict energy value of compound feeds for swine and ruminants. Anim. Feed Sci. Technol. 1996, 8401, 77–90. [Google Scholar] [CrossRef]
  23. Alomar, D.; Hodgkinson, S.; Abarzúa, D.; Fuchslocher, R.; Alvarado, C.; Rosales, E. Nutritional evaluation of commercial dry dog foods by near infrared reflectance spectroscopy. J. Anim. Physiol. Anim. Nutr. (Berl) 2006, 90, 223–229. [Google Scholar] [CrossRef] [PubMed]
  24. De Marchi, M.; Penasa, M.; Zidi, A.; Manuelian, C.L. Invited review: Use of infrared technologies for the assessment of dairy products—Applications and perspectives. J. Dairy Sci. 2018, 101, 10589–10604. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Schirmer, M.; Jekle, M.; Becker, T. Starch gelatinization and its complexity for analysis. Starch/Staerke 2015, 67, 30–41. [Google Scholar] [CrossRef]
  26. AOAC. Official Methods of Analysis, 20th ed.; Latimer, G.W., Jr., Ed.; AOAC International: Rockville, MD, USA, 2016. [Google Scholar]
  27. Shenk, J.S.; Westerhaus, M.O.; Abrams, S. Protocol for NIR calibrations: Monitoring analysis results and recalibration. In Near Infrared Spectroscopy (NIRS): Analysis of Forage Quality; Martens, G.C., Shenk, J.S., Barton, F.E., Eds.; USDA Agriculture Handbook No 643 (revised with supplements); U.S. Department of Agriculture: Washington, DC, USA, 1989; pp. 104–110. [Google Scholar]
  28. Williams, P.; Sobering, D. Comparison of commercial near infrared transmittance and reflectance instruments for analysis of whole grains and seeds. J. Near Infrared Spectrosc. 1993, 1, 25–32. [Google Scholar] [CrossRef]
  29. The European Pet Food Industry (FEDIAF). Nutritional Guidelines for Complete and Complementary Pet Food for Cats and Dogs; FEDIAF: Bruxelles, Belgium, 2018. [Google Scholar]
  30. Hall, M.B. Determination of dietary starch in animal feeds and pet food by an enzymatic-colorimetric method: Collaborative study. J. AOAC Int. 2015, 98, 397–409. [Google Scholar] [CrossRef]
  31. Tran, Q.D.; van Lin, C.G.J.M.; Hendriks, W.H.; van der Poel, A.F.B. Lysine reactivity and starch gelatinization in extruded and pelleted canine diets. Anim. Feed Sci. Technol. 2007, 138, 162–168. [Google Scholar] [CrossRef]
  32. Biagi, G.; Cipollini, I.; Grandi, M.; Pinna, C.; Vecchiato, C.G.; Zaghini, G. A new in vitro method to evaluate digestibility of commercial diets for dogs. Ital. J. Anim. Sci. 2016, 15, 617–625. [Google Scholar] [CrossRef] [Green Version]
  33. Lankhorst, C.; Tran, Q.D.; Havenaar, R.; Hendriks, W.H.; van der Poel, A.F.B. The effect of extrusion on the nutritional value of canine diets as assessed by in vitro indicators. Anim. Feed Sci. Technol. 2007, 138, 285–297. [Google Scholar] [CrossRef]
  34. Opitz, B.; Smith, P.M.; Kienzle, E.; Earle, K.E.; Maskell, I.E. Comparison of various methods of fiber analysis in pet foods. J. Nutr. 1998, 128, 2795S–2797S. [Google Scholar] [CrossRef]
  35. Cipollini, I. Pet food: Quality and Quality Improvement. Ph.D. Thesis, Alma Mater Studiorum-Università di Bologna, Bologna, Italy, 2008. [Google Scholar]
  36. Hervera, M.; Castrillo, C.; Albanell, E.; Baucells, M.D. Use of near-infrared spectroscopy to predict energy content of commercial dog food. J. Anim. Sci. 2012, 90, 4401–4407. [Google Scholar] [CrossRef]
  37. Goi, A.; Manuelian, C.L.; Currò, S.; De Marchi, M. Prediction of mineral composition in commercial extruded dry dog food by near-infrared reflectance spectroscopy. Animals 2019, 9, 640. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  38. Miller, C.E. Chemistry principles of near infrared technology. In Near Infrared Technology in the Agricultural and Food Industries, American Association of Cereal Chemist; Williams, P.C., Norris, K.H., Eds.; American Association of Cereal Chemists: St. Paul, MN, USA, 2001; pp. 19–37. [Google Scholar]
  39. Workman, J., Jr. The Handbook of Organic Compounds: NIR, IR, R, and UV-Vis Spectra Featuring Polymers and Surfactants; Academic Press: St Louis, MO, USA, 2000. [Google Scholar]
  40. Williams, C.; Norris, K.H. Near Infrared Technology in the Agricultural and Food Industries; American Association of Cereal Chemistry: St. Paul, MN, USA, 2001. [Google Scholar]
  41. Osborne, B.G.; Fearn, T.; Hindle, P.H. Practical NIR Spectroscopy with Applications in Food and Beverage Analysis; Longman Scientific and Technical: Harlow, UK, 1993. [Google Scholar]
  42. Hervera, M.; Baucells, M.D.; González, G.; Pérez, E.; Castrillo, C. Prediction of digestible protein content of dry extruded dog foods: Comparison of methods. J. Anim. Physiol. Anim. Nutr. (Berl.) 2009, 93, 366–372. [Google Scholar] [CrossRef] [PubMed]
  43. Williams, P. The RPD Statistic: A Tutorial Note. NIR News 2014, 25, 22–26. [Google Scholar] [CrossRef]
  44. Manuelian, C.L.; Currò, S.; Penasa, M.; Cassandro, M.; De Marchi, M. Prediction of minerals, fatty acid composition and cholesterol content of commercial cheeses by near infrared transmittance spectroscopy. Int. Dairy J. 2017, 71, 107–113. [Google Scholar] [CrossRef]
  45. Schmitt, S.; Garrigues, S.; de la Guardia, M. Determination of the mineral composition of foods by infrared spectroscopy: A review of a green alternative. Crit. Rev. Anal. Chem. 2014, 44, 186–197. [Google Scholar] [CrossRef]
  46. De Marchi, M.; Penasa, M.; Cecchinato, A.; Bittante, G. The relevance of different near infrared technologies and sample treatments for predicting meat quality traits in commercial beef cuts. Meat Sci. 2013, 93, 329–335. [Google Scholar] [CrossRef]
  47. Sinnaeve, G.; Dardenne, P.; Agneessens, R.; Biston, R. The Use of near Infrared Spectroscopy for the Analysis of Fresh Grass Silage. J. Near Infrared Spectrosc. 1994, 2, 79–84. [Google Scholar] [CrossRef]
  48. Manuelian, C.L.; Currò, S.; Visentin, G.; Penasa, M.; Cassandro, M.; Dellea, C.; Bernardi, M.; De Marchi, M. Technical note: At-line prediction of mineral composition of fresh cheeses using near-infrared technologies. J. Dairy Sci. 2017, 100, 6084–6089. [Google Scholar] [CrossRef]
Animals 10 00862 g001 550
Figure 1. Average raw spectra of dry dog pet food using (a) visible/near-infrared spectroscopy in reflectance mode and (b) near-infrared spectroscopy in transmittance mode.
Figure 1. Average raw spectra of dry dog pet food using (a) visible/near-infrared spectroscopy in reflectance mode and (b) near-infrared spectroscopy in transmittance mode.
Animals 10 00862 g001
Table
Table 1. Brief description of the commercial dry dog food samples included in the study.
Table 1. Brief description of the commercial dry dog food samples included in the study.
Main Carbohydrates SourceMain Protein SourcesDog SizeLife StageNumber of Packages
Oatsonly chickenmediumadult2
Cornchicken, duck, fish, lamb, rabbitsmall, medium, medium/large, largepuppy, adult25
Potatochicken, duck, horse, rabbit venisonsmall, medium/largepuppy, adult14
Peachicken, porksmall, medium/largeadult14
Ricefish, lamb, porksmall, medium, medium/large, largeadult13
Sorghumchicken, porksmall, medium, medium/large, largepuppy, adult13
Table
Table 2. Chemical composition (g/100 g dry matter (DM)) of the commercial dry dog food samples (n = 81) included in the study reported by the manufacturer.
Table 2. Chemical composition (g/100 g dry matter (DM)) of the commercial dry dog food samples (n = 81) included in the study reported by the manufacturer.
TraitMeanSDMinimumMaximumCV
Dry matter92.000.0092.0092.000.00
Crude protein28.984.6823.9140.2216.10
Crude fats15.903.4510.3321.7421.71
Crude Fibers3.622.592.2815.2271.56
Crude Ash7.321.342.509.7818.34
SD = standard deviation and CV = coefficient of variation; %.
Table
Table 3. Chemical composition (g/100 g DM) obtained by reference analyses of dry dog food samples (n = 81) included in this study.
Table 3. Chemical composition (g/100 g DM) obtained by reference analyses of dry dog food samples (n = 81) included in this study.
TraitMeanSDMinimumMaximumCV
Total starch34.227.6411.8746.7722.3
Gelatinized starch22.996.209.4936.9827.0
NDF17.486.528.2137.7437.3
ADF4.672.652.2817.3956.7
ADL1.850.820.434.5844.3
ADF = acid detergent fiber, ADL = acid detergent lignin, CV = coefficient of variation, NDF = neutral detergent fiber, and SD = standard deviation.
Table
Table 4. Fitting statistics of modified partial least square regression models using one-leave-out cross-validation (n = 81) for total and gelatinized starch, neutral detergent fiber (NDF), acid detergent fiber (ADF), and acid detergent lignin (ADL) content (g/100 g DM) in extruded dry dog food with visible and near-infrared reflectance spectroscopy (Vis-NIR) and near-infrared transmittance spectroscopy (NIT).
Table 4. Fitting statistics of modified partial least square regression models using one-leave-out cross-validation (n = 81) for total and gelatinized starch, neutral detergent fiber (NDF), acid detergent fiber (ADF), and acid detergent lignin (ADL) content (g/100 g DM) in extruded dry dog food with visible and near-infrared reflectance spectroscopy (Vis-NIR) and near-infrared transmittance spectroscopy (NIT).
TraitLFMeanSDR2CSECR2CrVSECrVRPDCrV
Vis-NIR, 400–2500 nm
Total starch834.897.340.990.590.971.166.33
Gelatinized starch923.046.100.980.890.951.324.62
NDF1016.815.970.971.030.931.523.93
ADF94.532.670.990.260.970.465.80
ADL91.810.820.980.110.930.223.73
NIT, 850–1050 nm
Total starch834.847.020.882.420.842.772.53
Gelatinized starch1022.826.150.832.550.772.942.09
NDF917.466.550.892.180.802.902.26
ADF104.101.210.800.540.670.691.75
ADL101.890.810.880.280.850.312.61
LF = optimal number of latent factors, R2C = coefficient of determination of calibration, R2CrV = coefficient of determination of cross-validation, RPDCrV = residual predictive deviation of cross-validation, SD = standard deviation, SEC = standard error of calibration, and SECrV = standard error of cross-validation.
Table
Table 5. Fitting statistics of modified partial least square regression models using external validation for total and gelatinized starch, neutral detergent fiber (NDF), acid detergent fiber (ADF), and acid detergent lignin (ADL) content (g/100 g DM) in extruded dry dog food with visible/near-infrared reflectance spectroscopy (Vis-NIR) and near-infrared transmittance spectroscopy (NIT).
Table 5. Fitting statistics of modified partial least square regression models using external validation for total and gelatinized starch, neutral detergent fiber (NDF), acid detergent fiber (ADF), and acid detergent lignin (ADL) content (g/100 g DM) in extruded dry dog food with visible/near-infrared reflectance spectroscopy (Vis-NIR) and near-infrared transmittance spectroscopy (NIT).
TraitCalibration Set (n = 60)Validation Set (n = 21)
LFSECrVR2CrVBiasSlopeSEPR2ExVRPDExV
Vis-NIR, 400–2500 nm
Total starch61.310.97−0.290.901.600.964.43
Gelatinized starch91.600.930.361.031.580.954.36
NDF101.680.91−1.121.041.630.954.31
ADF100.540.95−0.081.030.600.975.67
ADL100.230.89−0.041.020.280.842.46
NIT, 850–1050 nm
Total starch92.910.83−0.160.893.280.802.16
Gelatinized starch103.160.710.420.913.770.711.83
NDF82.640.780.110.962.900.741.97
ADF60.660.70−0.180.770.720.751.77
ADL90.260.83−0.120.770.380.721.71
LF = latent factors, R2ExV = coefficient of determination of external validation, RPDExV = residual predictive deviation of external validation, SD = standard deviation, SECrV = standard error of cross-validation, and SEP = standard error of external validation.

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Goi, A.; Manuelian, C.L.; Righi, F.; De Marchi, M. At-line Prediction of Gelatinized Starch and Fiber Fractions in Extruded Dry Dog Food Using Different Near-Infrared Spectroscopy Technologies. Animals 2020, 10, 862. https://doi.org/10.3390/ani10050862

AMA Style

Goi A, Manuelian CL, Righi F, De Marchi M. At-line Prediction of Gelatinized Starch and Fiber Fractions in Extruded Dry Dog Food Using Different Near-Infrared Spectroscopy Technologies. Animals. 2020; 10(5):862. https://doi.org/10.3390/ani10050862

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Goi, Arianna, Carmen L. Manuelian, Federico Righi, and Massimo De Marchi. 2020. "At-line Prediction of Gelatinized Starch and Fiber Fractions in Extruded Dry Dog Food Using Different Near-Infrared Spectroscopy Technologies" Animals 10, no. 5: 862. https://doi.org/10.3390/ani10050862

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