Tytuł artykułu
Autorzy
Identyfikatory
Warianty tytułu
NIR on-line spectroscopy in food production system control
Języki publikacji
Abstrakty
Cennych informacji nt. składu żywności może dostarczyć wnikliwe przestudiowanie widm w podczerwieni (IR) uzyskanych za pomocą spektrometrów IR. W analizie żywności wykorzystuje się najczęściej spektroskopię w bliskiej podczerwieni (NIR). Bez wątpienia największą zaletą spektroskopii NIR jest szybkość analizy, możliwość uzyskania kilku wyników w tym samym czasie oraz brak konieczności przygotowywania próbek. Zastosowanie systemów NIR on-line umożliwia ciągłe kontrolowanie procesu produkcyjnego w czasie rzeczywistym, precyzyjne jego sterowanie, a w konsekwencji optymalizację.
Reliable information about food composition can be provided by careful studying infrared (IR) spectrum obtained by the IR spectrometers. Near infrared (NIR) spectroscopy is the most commonly used for chemical analysis of food. Quickness of analysis and the possibility to obtain many results at a time are undoubtedly the greatest advantages of the NIR spectroscopy. There is no necessity of preparing samples for analysis. The use of NIR on-line systems provides the possibility to check the production process in real time on a constant basis, its control with a high precision, and consequently its optimization.
Wydawca
Czasopismo
Rocznik
Tom
Strony
26--30
Opis fizyczny
Bibliogr. 55 poz.
Twórcy
autor
autor
- Katedra Inżynierii Żywności i Organizacji Produkcji, SGGW, Warszawa
Bibliografia
- [1] Adamopoulos K.G., Goula A.M., Petropakis H.J. Quality control during processing of Feta cheese NIR application. Journal of Food Composition and Analysis, 14 (2001), 431-440.
- [2] Aloma, D., Gallo C., Castaneda M., Fuchslocher R. Chemical and discriminate analysis of bovine meat by near infrared reflectance spectroscopy (NIRS). Meat Science, 63 (2003), 441-450.
- [3] Batten G. D. Plant analysis using near infrared reflectance spectroscopy: The potential and the limitation. Australian Journal of Experimental Agriculture, 38 (1998), 697-706.
- [4] Berardo N., Pisacane V., Battilani P., Scandolara A., Pietri A., Marocco A. Rapid detection of kernel rots and mycotoxins in maize by near-infrared reflectance spectroscopy. Journal of Agricultural and Food Chemistry, 53 (2005), 8128-8134.
- [5] Bramble T., Herrman T.J., Loughin T., Dowell F., Single kernel protein variance structure in commercial wheat fields in Western Kansas. Crop Science, 42 (2002), 1488-1492.
- [6] Brennan D., Alderman J., Sattler L., O'Connor B., O'Mathuna C. Issues in development of NIR micro spectrometer system for online process monitoring of milk product. Measurement,33 (2003), 67-74.
- [7] Bruun S.W., Sřndergaard I., Jacobsen S. Analysis of protein structures and interactions in complex food by near-infrared spectroscopy. 1. Gluten Powder. Journal of Agricultural and Food Chemistry, 55 (2007a), 7234-7243.
- [8] Bruun S.W., Sřndergaard I., Jacobsen S. Analysis of protein structures and interactions in complex food by near-infrared spectroscopy. 2. Hydrated Gluten. Journal of Agricultural and Food Chemistry, 55 (2007b), 7244-7251.
- [9] Burns D.A., Ciurczak E.W. W Handbook of Near- Infrared Analysis (wydanie drugie). Vol. 27, Marcel Dekker, New York, 2001, 729-782.
- [10] Campbell M.R., Mannis S.R., Port H.A., Zimmerman A.M., Glover D.V. Prediction of starch amylose content versus total grain amylose content in corn by near-infrared transmittance spectroscopy. Cereal Chemistry, 76 (1999), 552-557.
- [11] Choi C.H. Development of apple sorter by soluble solid content using photodiodes. Proceedings of Winter Conference of KSAM, Suwon, 3 (1) (1998), 362-367.
- [12] Clark D.H., Short R.E. Comparison of AOAC and light spectroscopy analyses of uncooked, ground beef. Journal of Animal Science, 72 (1994), 925-931.
- [13] De Temmerman J., Saeys, W., Nicolai B., Ramon H. Near infrared reflectance spectroscopy as a tool for the in-line determination of the moisture concentration in extruded semolina pasta. Biosystems Engineering, 97(2007), 313-321.
- [14] Delwiche S.R. Protein content of single kernels of wheat by near infrared reflectance spectroscopy. Journal of Cereal Science, 27 (1998), 241-254.
- [15] Downey G., Mcintyre P., Davies A.N. Detecting and quantifying sunflower oil adulteration in extra virgin olive oils from the Eastern Mediterranean by visible and near-infrared spectroscopy. Journal of Agricultural and Food Chemistry, 50 (2002), 5520-5525.
- [16] Elgar H.J., Watkins C.B., Lallu N. Harvest date and crop load effects on a carbon dioxide-related storage injury of 'Braeburn' apple. Horticulture Science, 34 (1999), 305-309.
- [17] Engel R., Long D., Carlson G. On-the-go grain protein sensing is near. Better Crops with Plant Food, 81 (1997), 20-23.
- [18] Furniss B.S., Hannaford A.J., Rogers V., Smith P.W.G., Tatchell A.R. Vogels Textbook of Practical Organic Chemistry. (fourth edition). Longman Group, Londyn 1984.
- [19] Geesink G.H., Schreutelkamp F.H., Frankhuizen R., Vedder H.W., Faber N.M., Kranen R.W., Gerritzen M.A. Prediction of pork quality attributes from near infrared reflectance spectra. Meat Science, 65 (2003), 661-668.
- [20] Greensill C.V., Newman D.S. An experimental comparison of simple NIR spectrometers for fruit grading applications. Applied Engineering in Agriculture, 17 (2001), 69-76.
- [21] Hahn F. Spectral bandwidth effect on a Rhizopus stolonifer spores detector and its on-line behavior using red tomato fruits. Canadian Biosystems Engineering, 46 (2004), 349-354.
- [22] He D.J., Maekawa T., Morishima H. Detecting device for on-line detection of internal quality of fruits using near infrared spectroscopy and the related experiments. Transactions of the Chinese Society of Agricultural Engineering, 17 (2001), 146-148.
- [23] Hildrum K.I., Nilsen B.N., Mielnik M., Naes T. Prediction of sensory characteristics of beef by near-infrared spectroscopy. Meat Science, 38 (1994), 67-80.
- [24] Hildrum K.I., Nilsen B.N., Westad F., Wahlgren N.M. In-line analysis of ground beef using a diode array near infrared instrument on a conveyor belt. Journal of Near Infrared Spectroscopy, 12 (2004), 367-376.
- [25] Isaksson T., Nilsen B.N., TŘgersen G., Hammond R.P., Hildrum K.I. On-line, proximate analysis of ground beef directly at a meat grinder outlet. Meat Science, 43 (1996), 245-253.
- [26] Isaksson T., TŘgersen G., Iversen A., Hildrum K.I. Nondestructive determination of fat, moisture and protein in salmon fillets by use of near-infrared diffuse spectroscopy. Journal of the Science of Food and Agriculture, 69 (1995), 95-100.
- [27] Kawamura S., Natsuga M., Itoh K. Determination of undried rough rice constituent content using near-infrared transmission spectroscopy. Transactions of the ASAE, 42 (1999), 813-818.
- [28] Kawamura S., Natsuga M., Takekura K., Itoh K. Development of an automatic rice-quality inspection system. Computers and Electronics in Agriculture, 40(2003a), 115-126.
- [29] Kawamura S., Tsukahara M., Natsuga M., Itoh K. On-line near infrared spectroscopic sensing technique for assessing milk quality during milking. Transactions of the ASAE (2003b), Paper Number: 033026.
- [30] Kawano S., Fujiwara T., Iwamoto M. Nondestructive determination of sugar contents in satsuma mandarin using near infrared (NIR) transmittance. Journal of the Japanese Society for Horticultural Science, 62 (1993), 465-470.
- [31] Kawano S., Watanabe H., Iwamoto M. Determination of sugar content in intact peaches by near infrared spectroscopy with fiber optics in interactance mode. Journal of the Japanese Society for Horticultural Science, 61(1992), 445-451.
- [32] Kawasaki M., Kawamura S., Nakatsuji H., Natsuga M. Online real-time monitoring of milk quality during milking by near infrared spectroscopy, ASAE Meeting Presentation (2005), Paper Number: 053045.
- [33] Kays S.E., Windham W.R., Barton F.E. Prediction of totaldietary fiber in cereal products using near-infrared reflectance spectroscopy. Journal of Agricultural and Food Chemistry, 44 (1996), 2266-2271.
- [34] Kays S.J. Nondestructive quality evaluation of intact, high moisture products. NIR News, 10 (1999), 12-15.
- [35] Keener K.M., Stroshine R.L., Nyenhuis J.A. Evaluation of low field (5.40 MHz) proton magnetic resonance measurements of Dw and T2 as methods of non-destructive quality evaluation of apples. Journal of the American Society for Horticultural Science, 124(1999), 289-295.
- [36] Lau O.L., Lane W.D. Harvest indices, storability, and post storage refrigeration requirement of 'Sunrise' apple. Horticulture Science 33, (1998), 302-304.
- [37] Maertens K., Reyns P., De Baerdemaeker J. On-line measurement of grain quality with NIR technology. Transactions of the ASAE, 47 (2004), 1135-1140.
- [38] Marquez J. Monitoring carotenoid and chlorophyll pigments in virgin olive oil by visible-near infrared transmittance spectroscopy. Online application. Journal of Near Infrared Spectroscopy, 11 (2003), 219-226.
- [39] Massie D. R., Norris, K. H. Spectral reflectance and transmittance properties of grain in the visible and near infrared. Transactions of ASAE, 8 (1965), 598-600.
- [40] McClure W.F. W Near-infrared Spectroscopy in Food Science and Technology, Wiley-Interscience, Hoboken, NJ, 2007, pp.1-10.
- [41] Montes J.M., Utz H.F., Schipprack W., Kusterer B., Muminovic J., Paul C., Melchinger A.E. Near-infrared spectroscopy on combine harvesters to measure maize grain in dry matter content and quality parameters. Plant Breeding, 125 (2006), 591-595.
- [42] Ng C.L., Wehling R.L., Cuppett S.L. Method for determining frying oil degradation by near-infrared spectroscopy. Journal of Agricultural and Food Chemistry, 55 (2007), 593-597.
- [43] Ozdemir D. Genetic multivariate calibration for near infrared spectroscopic determination of protein, moisture, dry mass, hardness and other residues of wheat. International Journal of Food Science and Technology, 41 (2006), 12-21.
- [44] Prevolnik M., Candek-Potokar M., Skorjanc D., Velikonja-Bolta S. S., krlep M., Znidarsic T., Babnik D. Predicting intramuscular fat content in pork and beef by near infrared spectroscopy. Journal of Near Infrared Spectroscopy, 13 (2005), 77-86.
- [45] Prieto N., Andres S., Giraldez F.J., Mantecon A.R., Lavn P. Potential use of near infrared reflectance spectroscopy (NIRS) for the estimation of chemical composition of oxen meat samples. Meat Science, 74 (2006), 487-496.
- [46] Reyns P., Spaepen P., De Baerdemaeker J. Site-specific relationship between grain quality and yield. Precision Agriculture, 2 (3) (2000), 231-246.
- [47] Savenije B., Geesink G.H., van der Palen J.G.P., Hemke G. Prediction of pork quality using visible/near-infrared reflectance spectroscopy. Meat Science, 73 (2006), 181-184.
- [48] Togersen G., Isaksson T., Nilsen B.N., Bakker E.A., Hildrum K.I. On-line NIR analysis of fat, water and protein in industrial scale ground meat batches. Meat Science, 51 (1999), 97-102.
- [49] Volz R.K., Biasi W.V., Grant J.A., Mitcham E.J. Prediction of controlled atmosphere-induced flesh browning in 'Fuji' apple. Postharvest Biology and Technology, 13 (1998), 97-107.
- [50] Wehling R.L., Jackson D.S., Hamaker B.R. Prediction of corn dry-milling quality by near-infrared spectroscopy. Cereal Chemistry, 73 (1996), 543-546.
- [51] Welle R., Greten W., Muller T., Weber G., Wehrmann H. Application of near infrared spectroscopy on-combine in corn grain breeding. Journal of Near Infrared Spectroscopy, 13 (2005), 69-76.
- [52] Workman J.J. Review of process and non-invasive near-infrared and infrared spectroscopy: 1993-1999. Applied Spectroscopy Review, 34(1&2) (1999),1 - 89.
- [53] Xie L., Ying Y., Ying T. Combination and comparison of chemometrics methods for identification of transgenic tomatoes using visible and near-infrared diffuse transmittance technique. Journal of Food Engineering, 82 (3) (2007a), 395-401.
- [54] Xie L., Ying Y., Ying T., Yu H., Fu X. Discrimination of transgenic tomatoes based on visible/near-infrared spectra. Analytica Chimica Acta, 584 (2) (2007b), 379-384.
- [55] Yildiz G., Wehling R.L., Cuppett S.L. Monitoring PV in corn and soybean oils by NIR spectroscopy. Journal of the American Oil Chemists' Society, 79 (11) (2002), 1085-1089.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-LOD1-0019-0040