Prediction of minerals, fatty acid composition and cholesterol content of commercial cheeses by near infrared transmittance spectroscopy

Manuelian, Carmen L.; Currò, Sarah; Penasa, M.; Cassandro, Martino; Marchi, Massimo De

doi:10.1016/j.idairyj.2017.03.011

Cited by 27 publications

(33 citation statements)

References 32 publications

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“…Kraggerud et al (2014) also attempted to predict sensory traits using Fourier-transform MIR spectroscopy in transmittance mode or NIR spectroscopy in reflectance mode (Table 6), but with unsatisfactory results, which was expected because sensory attributes are not directly linked to an absorption infrared region or to organic molecules, and the reference values are based on a subjective evaluation. Minerals in cheese were successfully predicted using infrared technology when considering a wide variety of cheeses (Manuelian et al, 2017a), achieving an RPD greater than 3 for Ca, P, S, Mg, Zn, and Cu (Table 7), probably because part of minerals in cheese are linked to organic complexes that can be detected through infrared spectroscopy, as mentioned above. Prediction models for Mozzarella and Stracchino cheeses were probably poor due to the lower mineral content and lower variability (Manuelian et al, 2017b).…”

Section: Cheesementioning

confidence: 94%

“…Following Williams (2014), RPD between 2.4 and 3 is adequate for a rough screening when analyzing difficult matrixes such as cheese. For example, the RPD of prediction models in external validation for Ca, Na, P, S, and Mg ranged from 2.35 to 3.73 and from 1.40 to 2.03 in Manuelian et al (2017a) and Manuelian et al (2017b), respectively; the difference can be attributed to the concentration of minerals and the variability of the reference data used to build the calibration equations. Indeed, the first study included 19 different varieties of soft, semi-hard, and hard cheeses, thus a greater concentration and variability of minerals, and the second study developed the calibration models on only one type of soft cheese.…”

Section: Concentration Variability and Unit Of Measurementmentioning

confidence: 99%

“…Almost all prediction models have been built using partial least squares (PLS) regression. Only 4 studies used modified PLS (MPLS) regression (González-Martín et al, 2014;Revilla et al, 2016;Currò et al, 2017;Manuelian et al, 2017a), and one used kernel PLS regression (Lin et al, 2014). The MPLS approach is considered more accurate and stable than the PLS approach (González-Martín et al, 2014) because the algorithm for MPLS considers the standardized residuals after each factor before moving to the next factor (Meagher et al, 2007).…”

Section: Cheesementioning

confidence: 99%

“…Prediction equations for milk powder Table 7. Fitting statistics 1 of the prediction models for minerals using near-infrared transmittance (NIT) spectroscopy and Fourier-transform near-infrared spectroscopy in reflectance mode (FT-NIR.R) in Mozzarella and Stracchino cheeses (Manuelian et al, 2017b), and other cheeses 2 (Manuelian et al, 2017a) Trait…”

Section: Butter Milk Powder and Yogurtmentioning

confidence: 99%

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Invited review: Use of infrared technologies for the assessment of dairy products—Applications and perspectives

Marchi

Penasa

Zidi

et al. 2018

Journal of Dairy Science

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Dairy products are important sources of nutrients for human health and in recent years their consumption has increased worldwide. Therefore, the food industry is interested in applying analytical technologies that are more rapid and cost-effective than traditional laboratory analyses. Infrared spectroscopy accomplishes both criteria, making real-time determination feasible. However, it is crucial to ensure that prediction models are accurate before their implementation in the dairy industry. In the last 5 yr, several papers have investigated the feasibility of mid-and near-infrared spectroscopy to determine chemical composition and authenticity of dairy products. Most studies have dealt with cheese, and few with yogurt, butter, and milk powder. Also, the use of near-infrared (in reflectance or transmittance mode) has been more prevalent than mid-infrared spectroscopy. This review summarizes recent studies on infrared spectroscopy in dairy products focusing on difficult to determine chemical components such as fatty acids, minerals, and volatile compounds, as well as sensory attributes and ripening time. Promising equations have been developed despite the low concentration or the absence of specific absorption bands (or both) for these compounds.

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Section: Cheesementioning

confidence: 94%

Section: Concentration Variability and Unit Of Measurementmentioning

confidence: 99%

Section: Cheesementioning

confidence: 99%

Section: Butter Milk Powder and Yogurtmentioning

confidence: 99%

See 2 more Smart Citations

Invited review: Use of infrared technologies for the assessment of dairy products—Applications and perspectives

Marchi

Penasa

Zidi

et al. 2018

Journal of Dairy Science

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show abstract

“…For example, some authors evaluated the potential of NIRS to predict the total antioxidant capacity of different cheeses, ending up with positive conclusions and perspectives [36]. Moreover, R 2 EV of 0.50 for cholesterol content has been reported [37]; as regards major minerals, the same authors found R 2 EV from 0.65 (potassium) to 0.94 (phosphorus). Nevertheless, the scientific community disclosed the limits of infrared techniques in predicting cheese sensory characteristics, such as pasty, grainy, solubility, cohesion, firmness on chewing, flavour intensity [12].…”

Section: Application Of Infrared Spectroscopy In Milkmentioning

confidence: 99%

Novel applications of infrared technologies in dairy industry

Massimo¹,

Costa²,

Goi³

et al. 2019

Adv Techol

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Dairy industry is economically relevant in many countries, and milk and its derived products are important sources of nutrients both in developed and developing countries. Thus, the dairy industry is interested in the application of rapid and cost-effective technologies for raw product evaluation, processing control and labeling. One of the most widespread technologies for milk and dairy product analysis is the infrared spectroscopy, mainly in the mid-infrared (MIRS) and near-infrared (NIRS) spectra regions. Other than standard composition, the assessment of milk technological traits is of particular interest in Europe and, traditionally, milk coagulation traits and acidity, milk protein and mineral composition have been determined using time-consuming laboratory methods. Recently, the development of specific MIRS prediction models for such milk traits has allowed the collection of coagulation information at herd and population level for milk quality payment and genetic selection purposes, respectively. Therefore, MIRS technology offers the possibility to conduct largescale phenotypic, genetic and genomic studies, even through a posteriori prediction using historical milk spectra stored in the laboratories. Cheese provides essential nutrients such as fatty acids, minerals and vitamins, and its consumption in Europe has grown in the last decade. Nevertheless, due to the negative consequences of the Western diet on the cardiovascular system mainly related to the excessive intake of saturated fatty acids and sodium, the declaration of salt and saturated fatty acids contents in the product's label has become mandatory. In the last years, the dairy industry has successfully developed and implemented NIRS prediction models for at-line and inexpensive quantification of minerals and fatty acids of commercial cheeses in order to quickly incorporate this information on the label of dairy products. The development of MIRS and NIRS prediction models for milk and cheese traits has opened new opportunities in the field of dairy products, spanning from genetic selection to technological efficiency.

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