TY - JOUR
T1 - Technical note
T2 - At-line prediction of mineral composition of fresh cheeses using near-infrared technologies
AU - Manuelian, C. L.
AU - Currò, S.
AU - Visentin, G.
AU - Penasa, M.
AU - Cassandro, M.
AU - Dellea, C.
AU - Bernardi, M.
AU - De Marchi, M.
N1 - Publisher Copyright:
© 2017 American Dairy Science Association
PY - 2017/8
Y1 - 2017/8
N2 - Milk and dairy products are important sources of macro- and trace elements for human health. However, fresh cheeses usually have a lower mineral content than other cheeses, and this makes mineral prediction more difficult. Although mineral prediction in several food matrices using infrared spectroscopy has been reported in the literature, very little information is available for cheeses. The present study was aimed at developing near-infrared reflectance (NIR, 866–2,530 nm) and transmittance (NIT, 850–1,050 nm) spectroscopy models to predict the major mineral content of fresh cheeses. We analyzed samples of mozzarella (n = 130) and Stracchino (n = 118) using reference methods and NIR and NIT spectroscopy. We developed prediction models using partial least squares regression analysis, and subjected them to cross- and external validation. Average Na content was 0.15 and 0.22 g/100 g for mozzarella and Stracchino, respectively. The NIR and NIT spectroscopy performed similarly, with few exceptions. Nevertheless, none of the prediction models was accurate enough to replace the current reference analysis. The most accurate prediction model was for the Na content of mozzarella cheese using NIT spectroscopy (coefficient of determination of external validation = 0.75). We obtained the same accuracy of prediction for P in Stracchino cheese with both NIR and NIT spectroscopy. Our results confirmed that mineral content is difficult to predict using NIT and NIR spectroscopy.
AB - Milk and dairy products are important sources of macro- and trace elements for human health. However, fresh cheeses usually have a lower mineral content than other cheeses, and this makes mineral prediction more difficult. Although mineral prediction in several food matrices using infrared spectroscopy has been reported in the literature, very little information is available for cheeses. The present study was aimed at developing near-infrared reflectance (NIR, 866–2,530 nm) and transmittance (NIT, 850–1,050 nm) spectroscopy models to predict the major mineral content of fresh cheeses. We analyzed samples of mozzarella (n = 130) and Stracchino (n = 118) using reference methods and NIR and NIT spectroscopy. We developed prediction models using partial least squares regression analysis, and subjected them to cross- and external validation. Average Na content was 0.15 and 0.22 g/100 g for mozzarella and Stracchino, respectively. The NIR and NIT spectroscopy performed similarly, with few exceptions. Nevertheless, none of the prediction models was accurate enough to replace the current reference analysis. The most accurate prediction model was for the Na content of mozzarella cheese using NIT spectroscopy (coefficient of determination of external validation = 0.75). We obtained the same accuracy of prediction for P in Stracchino cheese with both NIR and NIT spectroscopy. Our results confirmed that mineral content is difficult to predict using NIT and NIR spectroscopy.
KW - mineral
KW - mozzarella cheese
KW - sodium
KW - Stracchino cheese
UR - http://www.scopus.com/inward/record.url?scp=85020725013&partnerID=8YFLogxK
U2 - 10.3168/jds.2017-12634
DO - 10.3168/jds.2017-12634
M3 - Article
C2 - 28624277
AN - SCOPUS:85020725013
SN - 0022-0302
VL - 100
SP - 6084
EP - 6089
JO - JOURNAL OF DAIRY SCIENCE
JF - JOURNAL OF DAIRY SCIENCE
IS - 8
ER -