Original Research ArticleCompositional method for measuring the nutritional label components of industrial pastries and biscuits based on Vis/NIR spectroscopy
Introduction
Consumption of foods with a high glycemic index is hypothesized to contribute to insulin resistance, which is associated with increased risk of diabetes mellitus, obesity, cardiovascular disease, and some cancers (Neuhouser et al., 2006). Besides, there is an increasing prevalence of diabetes mellitus worldwide (Wild et al., 2004). Carbohydrates are the major part both in bakery, pastry and biscuit products, while total fat is variable. Their protein content is in general not relevant. The fat composition of the diet has also important nutritional implications (Schwingshackl and Hoffmann, 2014). The unfavorable effects of SFA on cardiovascular health and the positive effects of MUFA, are widely proven (Hernáez et al., 2017; Schwingshackl and Hoffmann, 2014). Thus, some pastry producers have replaced SFA with MUFA, often by using high oleic sunflower.
Saturated fatty acids are those without any unsaturation within their chain. Pastry includes usually as major SFA palmitic acid (C16:0), stearic acid (C18:0), and small quantities of margaric acid (C17:0), arachidic acid (C20:0), and behenic acid (C22:0) (Yolci-Omeroglu and Ozdalb, 2020). MUFA are those fatty acids which carbon chain has a single unsaturation. Palmitoleic acid (C16:1) and oleic acid (C18:1) are the major MUFA in pastry products, according to the last referred authors. Oleic acid unsaturation locates after the number 9 carbon, commonly named ω-9. PUFA are those fatty acids containing more than one double bond in their backbone. Good human health requires diets with small quantities of these compounds, such as the essential fatty acids linoleic (C18:2), ω-6, and linolenic (C18:3), this last called ω-3. Linoleic acid is often present in pastry products (Yolci-Omeroglu and Ozdalb, 2020).
The standards of the European Union (CE, 2011), applicable since 13 December 2016, settles the duty to include nutritional information as part of the compulsory food information to the consumer. This includes energy value, total fat contents, saturated fatty acids (SFA), carbohydrates, sugars, proteins and salt. The rule considers as voluntary nutritional information other nutrients such as mono-unsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA), trans-fatty acids, vitamins, healthy compounds, and other possible relevant compounds, as cholesterol. Therefore, pastry and biscuit products must include nutritional information on their label. The most common information included up to date is total fat and saturated fat, although some products also provide information on monounsaturated and polyunsaturated fat.
Verifying the nutritional composition of different batches of pastry using fast and reliable techniques is a target both for the industry and the administration, responsible for controlling food quality. Among the various non-destructive techniques able to provide solutions to these needs, near infrared spectroscopy stands out for its important achievements so far. There is extensive literature on NIRS techniques for assessing food quality (Haiyan and Yong, 2007; Manley, 2014; Baeten et al., 2016; among others). Food authentication is also important aim of NIRS (v. g. Reid et al., 2006; Callao and Ruisanchez, 2018; Mendez et al., 2019).
One of the first studies about near infrared spectroscopy (NIRS) on pastry was intended to determine fat, protein and water content (Kaffka et al., 1982). A later study revealed the possibility of measuring fiber content (Kays et al., 1997, 1998). Vines et al. (2005) reported measurement of total fat by NIRS on cereal food. A study on rapid analysis of chemical composition in intact and milled rice biscuits is found in Wimonsiri et al. (2017). Despite the facts above, using NIRS for the specific purpose of determining the components of a food nutritional label has been reported in one only paper (Fernández-Cabanas et al., 2011). This was on the determination of the fatty acid profile in pork dry-cured sausages. Additionaly, one more study refers to Near-infrared reflectance spectroscopy (NIRS) for the mandatory labelling of compound feedingstuffs (Pérez-Marín et al., 2004). These studies did not mention the need to adequately treat reference compositional data. In fact, the integration of specific methods for compositional data in NIRS techniques has been absent until not long ago. A novel technique of NIRS in combination with compositional statistical methods was introduced recently for nutritional labelling of olive oil (Cayuela-Sánchez et al., 2019).
Compositional data are multivariate data representing fractions or parts of some whole and, thus, carry only relative information. This implies that values in each part have meaning only in relation to the other parts and specialized statistical analysis methods accounting for these features are required. Nutritional compositions, consisting of fractions measured across mutually exclusive categories commonly scaled as percentages, fall into this definition. Amongst others, compositional data bring some difficulties to the use of ordinary correlations and distances, since these ignore the relative scale and the constraints they are subject to. Well-known issues include negative bias in correlation analysis, singularity of covariance matrices in regression analysis, predictions beyond the range of possible values (e.g. values outwith the [0, 100] interval when working in percentage units) or results which depend on the scale of measurement. Obviously, these issues can potentially lead to misleading scientific conclusions. A solid methodological framework based on using log-ratios between parts of a composition was presented in the seminal work by Aitchison (1986) and has been further elaborated and successfully applied in different scientific fields over the past 30+ years. Working with log-ratios focuses the data analysis on the relative information, guarantees that results do not depend on the scale of measurement (i.e. on whether it is percentages, proportions, ppm, mg/g, etc.) and, conveniently, facilitates the use of ordinary statistical methods which results can be transferred back in terms of the original compositions if required (Aitchison, 1986; Van den Boogaart and Tolosana-Delgado, 2013; Pawlowsky-Glahn et al., 2015).
NIRS offers several important advantages, as it is a rapid, non-destructive and potentially multi-parametric method. In addition, NIRS does not need solvents or reagents, avoiding a significant expense and protecting the environment. In this study we present a new approach to determine the nutritional makeup of industrial pastries and biscuits for measuring their nutritional label components. It is based on fitting PLS calibration models on Vis/NIR spectra which explicitly account for the compositional nature of the reference data and avoids analytical artifacts.
Section snippets
Pastry and biscuit samples
The robustness of NIRS calibrations depends on the statistic range of the analyzed features. Therefore, the choice of the commercial product types for which predictive models are implemented is a critical factor.
Two separate tests were conducted with commercial pastry and biscuit products. Two independent batches of samples were selected by the authors in different superstore markets of Seville (Spain) from different manufacturers for both types of products. The total number of samples of
Pastry and biscuit products spectra
Near-infrared spectra show various overlapping bands, because of their first and second overtones and a combination of fundamental vibrations, mainly carbon–hydrogen (Shenk et al., 2001). The large peak observed at 1900 nm corresponds to water. According to Salgó and Gergely (2012), carbohydrates peaks are detected in three wavelength regions: (i) between 1585 and 1595 nm, (ii) from 2270 to 2280 nm, and (iii) from 2325 to 2335 nm. This latter probably relates to the combination of the bond
Conclusions
This study introduces a methodological basis to measure the nutritional composition of industrial pastries and biscuits for labelling purposes, using Vis-NIRS and taking into account the compositional nature of the reference data. The data modelling was conducted so that it took into account the intrinsic relative and inter-dependent nature of percentage nutritional compositions and this was incorporated within the well-known framework of PLS calibration. In spite of the limited number of
CRediT authorship contribution statement
José A. Cayuela-Sánchez: Conceptualization, Methodology, Formal analysis, Writing - original draft, Writing - review & editing, Supervision, Project administration. Javier Palarea-Albaladejo: Methodology, Validation, Formal analysis, Writing - review & editing. Tatiana P. Zira: Investigation. Elena Moriana-Correro: Investigation.
Declaration of Competing Interest
The authors declare no competing interests.
Acknowledgements
This work has been done without specific funding. J. A. Cayuela-Sánchez thank the Spanish Council for Scientific Research for supporting his activity as staff. J. Palarea-Albaladejo was partly supported by the Scottish Government’s Rural and Environment Science and Analytical Services Division and the Spanish Ministry of Economy and Competitiveness [Ref: RTI2018-095518-B-C21].
References (32)
- et al.
Olive oil nutritional labeling by using Vis/NIR spectroscopy and compositional statistical methods
Innov. Food Sci. Emerg. Technol.
(2019) - et al.
Rapid determination of the fatty acid profile in pork dry-cured sausages by NIR spectroscopy
Food Chem.
(2011) - et al.
Development of a glycemic index database for food frequency questionnaires used in epidemiologic studies
J. Nutr.
(2006) - et al.
Near-infrared reflectance spectroscopy (NIRS) for the mandatory labelling of compound feedingstuffs: chemical composition and open-declaration
Anim. Feed Sci. Technol.
(2004) - et al.
Recent technological advances for the determination of food authenticity
Trends Food Sci. Technol.
(2006) - et al.
Analysis of wheat grain development using NIR spectroscopy
J. Cereal Sci.
(2012) - et al.
Fatty acid composition of sweet bakery goods and chocolate products and evaluation of overall nutritional quality in relation to the food label information
J. Food Compos. Anal.
(2020) The Statistical Analysis of Compositional Data
(1986)- et al.
Near infrared spectroscopy for food and feed: a mature technique
Nir News
(2016) - et al.
An overview of multivariate qualitative methods for food fraud detection
Food Control
(2018)
Regulation (EU) No 1169/2011 of the European Parliament and of the Council of 25 October 2011 on the Provision of Food Information to Consumers
Near-infrared spectroscopy for monitoring starch hydrolysis
Appl. Spectrosc.
Isometric log-ratio transformations for compositional data analysis
Math. Geol.
Theory and application of near infrared reflectance spectroscopy in determination of food quality
Trends Food Sci. Technol.
The Mediterranean diet decreases LDL atherogenicity in high cardiovascular risk individuals: a randomized controlled trial
Mol. Nutr. Food Res.
Oil and fat classification by selected bands of near-infrared spectroscopy
Appl. Spectrosc.
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