Prediction of the chemical composition of freeze dried ostrich meat with near infrared reflectance spectroscopy

Viljoen, M.; Hoffman, Louwrens C.; Brand, T. S.

doi:10.1016/j.meatsci.2004.07.008

Cited by 34 publications

(18 citation statements)

References 14 publications

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“…The physical appearance of freezedried meat is more homogenous than raw minced meat, due to the influence of the fat on the particle size of raw minced meat samples (Kruggel et al, 1981). Some of the important wavelengths for water (1190; 1940 nm) (Osborne et al, 1993(Osborne et al, ), protein (16802050;2180 nm) (Osborne et al, 1993) andfat (1200;1734;1765;2310; 2345 nm) (Osborne et al, 1993) in freeze-dried mutton corresponded with wavelengths noted for the same constituents in freeze-dried ostrich meat (Viljoen et al, 2005), in spite of the differences in protein and fat composition of the different types of meat.…”

Section: Resultsmentioning

confidence: 89%

Prediction of the chemical composition of mutton with near infrared reflectance spectroscopy

Viljoen

Hoffman

Brand

2007

Small Ruminant Research

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Section: Resultsmentioning

confidence: 89%

Prediction of the chemical composition of mutton with near infrared reflectance spectroscopy

Viljoen

Hoffman

Brand

2007

Small Ruminant Research

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“…The modified PLSR models could predict pH, Near-infrared spectroscopy and imaging in food quality and safety 203 intramuscular fat, and color of pork quantitatively. Viljoen et al attempted to determine ash, dry matter, crude protein, and fat content in freeze-dried ostrich meat [175]. The PLSR models were accurate in predicting the crude protein and fat content but not for ash and dry matter.…”

Section: Grain Quality Inspectionmentioning

confidence: 99%

Near-infrared spectroscopy and imaging in food quality and safety

Wang

Paliwal

2007

Sens. & Instrumen. Food Qual.

143

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Over the last two decades, near-infrared spectroscopy (NIRS) has established itself as a non-destructive analytical technique in a variety of disciplines. However, recent technological advancements in hardware design and data mining techniques have unleashed the potential of NIRS to become a tool of choice for routine analyses of agricultural products. The current paper synthesizes the status of NIRS in the agri-food industry in terms of hardware and software development as well as the direction in which the NIRS research is headed. An extensive review of literature reveals that the emphasis on hardware development is focused on developing compact, robust, and portable spectrometers and hyperspectral imaging (HSI) systems. The software development on the other hand is geared towards developing better preprocessing, analyses, and modeling techniques using chemometrics, support vector machines, and artificial neural networks. The four main agri-food sectors identified to be the beneficiaries of this research revolution are grain quality monitoring; postharvest handling of fruits and vegetables; identification of contaminants in animal produce and feed; and food safety and authenticity. Apart from discussing the aforementioned topics, the paper also provides food scientists some working knowledge on parameters crucial to the performance of spectral and imaging systems. It is expected that further development of NIRS will help agricultural and food scientists to enhance the quality and safety of our food.

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“…For example, conventional chemical analysis of feeds will take two to three days, while a similar analysis can be completed in 2 -3 minutes by NIRS (Corson et al, 1999). However, calibration sets with insufficient distribution of the samples could lead to inaccurate calibrations (Viljoen et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

The use of near infrared spectroscopy (NIRS) to predict the chemical composition of feed samples used in ostrich total mixed rations

Swart¹,

Brand²,

Engelbrecht³

2012

SA J. An. Sci.

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The wet chemical analysis of feed samples is time consuming and expensive. Near infrared spectroscopy (NIRS) was developed as a rapid technique to predict the chemical composition of feeds. The prediction of accuracy of NIRS relies heavily on obtaining a calibration set which represents the variation in the main population, accurate laboratory analyses and the application of the best mathematical procedures. In this study NIRS was used to determine the chemical composition of total mixed rations (TMRs) used in ostrich diets. A sample population of 479 ostrich feed samples was used in the calibration and 94 samples were used in the independent validation of dry matter (DM), ash, crude protein (CP), ether extract (EE), crude fibre (CF), acid detergent fibre (ADF), neutral detergent fibre (NDF), gross energy (GE), calcium (Ca) and phosphorus (P). Coefficient of determination in validation (r 2 v ) and standard error of prediction (SEP) was satisfactory (r 2 v values higher than 0.80). Coefficient of determination and SEP values for CP, EE, CF, ADF, NDF and GE were 0.97% and 0.74%, 0.89% and 0.50%, 0.94% and 1.41%, 0.89% and 2.67%, 0.95% and 2.81% and 0.80% and 0.28 MJ/kg, respectively. Less accurate values (r 2 v below 0.80) were obtained for DM, ash, Ca and P being 0.57% and 0.28%, 0.67% and 1.29%, 0.43% and 0.59% and 0.49% and 0.11%, respectively. The study indicated that NIRS is a suitable tool for a rapid, non-destructive and reliable prediction of the chemical composition of ostrich TMRs.

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Prediction of the chemical composition of freeze dried ostrich meat with near infrared reflectance spectroscopy

Cited by 34 publications

References 14 publications

Prediction of the chemical composition of mutton with near infrared reflectance spectroscopy

Prediction of the chemical composition of mutton with near infrared reflectance spectroscopy

Near-infrared spectroscopy and imaging in food quality and safety

The use of near infrared spectroscopy (NIRS) to predict the chemical composition of feed samples used in ostrich total mixed rations

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