Near-infrared evaluation of wet mink diets

White, M.; Rouvinen‐Watt, Kirsti

doi:10.1016/j.anifeedsci.2003.08.006

Cited by 8 publications

(6 citation statements)

References 12 publications

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“…Other authors , developed equations for only one animal species and obtained better SECV values (0.62% and 0.56%, respectively) than those obtained here with the diode-array instrument. Nevertheless, White and Rouvinen-Watt evaluated feeds only intended for mink and recorded quite high SECV values (1.75%) for crude protein. However, when using the RPD statistic in order to better compare studies using populations with different mean and SD, it was observed that Xicatto et al reported RPD values of 2.67, not reaching the recommended minimum, while White and Rouvinen-Watt , whose calibration set had a higher SD, obtained RPD values (3.97) higher than 3 and close to that obtained here.…”

Section: Resultsmentioning

confidence: 97%

“…Nevertheless, White and Rouvinen-Watt evaluated feeds only intended for mink and recorded quite high SECV values (1.75%) for crude protein. However, when using the RPD statistic in order to better compare studies using populations with different mean and SD, it was observed that Xicatto et al reported RPD values of 2.67, not reaching the recommended minimum, while White and Rouvinen-Watt , whose calibration set had a higher SD, obtained RPD values (3.97) higher than 3 and close to that obtained here. Cizmar et al , using a larger number of samples ( N = 650) in calibration sets, obtained good SECV values (0.5%), but the RPD was slightly below 3 (2.68).…”

Section: Resultsmentioning

confidence: 97%

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Feasibility of Diode-Array Instruments To Carry Near-Infrared Spectroscopy from Laboratory to Feed Process Control

Fernández-Ahumada

Guerrero-Ginel

2008

J. Agric. Food Chem.

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Near-infrared calibrations were developed for the instantaneous prediction of the chemical and ingredient composition of intact compound feeds. Two rather different instruments were compared (diode array vs grating monochromator). The grating monochromator was used in a static mode in the laboratory, whereas the diode-array instrumentbetter adapted to online analysiswas placed on a conveyor belt to simulate measurements at a feed mill plant. Modified partial least squares (MPLS) equations were developed using the same set of samples analyzed in the two instruments. Sample set 1 ( N = 398) was used to predict crude protein (CP) and crude fiber (CF), while sample set 2 ( N = 393) was used for the prediction of one macroingredient (sunflower meal, SFM) and one microingredient (mineral-vitamin premix, MVP). The standard error of cross-validation (SECV) and the coefficient of determination (R2) values for CF were better using the monochromator instrument. However, results obtained for CP, SFM, and MVP using the samples analyzed in the diode-array instrument showed similar or even greater accuracy than those obtained using samples analyzed in the grating monochromator. The excellent predictive ability [R2> 0.95; RPD (ratio of standard deviation to SECV) > 3] obtained for CP, CF, and SFM opens the way for the online use of NIRS diode-array instruments for surveillance and monitoring in the manufacture, processing, and marketing of compound feeds. R2, RPD, and SECV values for MVP showed similar performance for both instruments. Although RPD values did not reach the minimum recommended for quantitative analysis, results are encouraging for an ingredient present in feed compounds in such very low amounts.

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Section: Resultsmentioning

confidence: 97%

Section: Resultsmentioning

confidence: 97%

Feasibility of Diode-Array Instruments To Carry Near-Infrared Spectroscopy from Laboratory to Feed Process Control

Fernández-Ahumada

Guerrero-Ginel

2008

J. Agric. Food Chem.

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“…Near infrared reflectance spectroscopy (NIRS) is one of the recent techniques being applied for such feed resource quality determination and further characterization. The method has been applied for various feed stuffs and parameters and the application for characterization both for ruminant and non ruminant animals have been studied extensively in the temperate environment (Gordon et al, 1998;Stuth et al, 2003;and White and Rouvinen-Watt, 2004). The technique is, thus far, noted to be one of the robust applications to estimate chemical entity and parameters like digestibility and degradability which are usually estimated by bioassays.…”

Section: Introductionmentioning

confidence: 99%

Near Infrared Reflectance Spectroscopy (NIRS) for determination of chemical entities of natural pasture from Ethiopia

Fekadu¹,

Bediye²,

Kehaliw³

et al. 2010

ABJNA

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In the mixed crop-livestock farming system of Ethiopia, natural pasture production is related to seasonal rainfall distribution which is restricted to only a few months of the year. Livestock owners in this country experience periods of excess forage in the rain season where preserving it through haymaking became tradition as a strategy for the periods of scarcity, particularly the dry season of a year. Forage can also be stored as standing hay, the cheapest way of conserving forage, in a place like pastoral and agro-pastoral production system where a decline in quality can be tolerated. Mixed grass-legume pasture samples from different agro-ecology were collected and portion of the samples were analyzed conventionally to determine their chemical composition (dry matter, ash, crude protein, neutral detergent fiber, acid detergent fiber, lignin) and in vitro dry matter digestibility to validate the NIRS calibration. NIRS was evaluated by the coefficient of determination in calibration (R 2), standard error of calibration (SEC), and standard error of cross validation (SECV). The results showed NIRS is extremely useful for predicting nutrient contents as compared to conventional chemical analysis methods which are not difficult to conduct, but they are expensive in terms of time and resources.

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“…In mixed diets, Almendingen et al (2000) demonstrated that CP and fat content of homogenized human diets could be accurately predicted by NIRS. Likewise, White and Rouvinen-Watt (2004) developed robust NIRS equations to predict the DM, CP and fat content of mixed diets for mink.…”

Section: Introductionmentioning

confidence: 99%

Utility of Near-Infrared Reflectance Spectroscopy to Predict Nutrient Composition and In Vitro Digestibility of Total Mixed Rations

Mentink

Hoffman

Bauman

2006

Journal of Dairy Science

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Total mixed ration (TMR) samples (n = 110) were analyzed for dry matter (DM), crude protein (CP), soluble CP, neutral detergent fiber (NDF), NDF CP, starch, ash, fat, total ethanol-soluble carbohydrate, and nonfiber carbohydrate (NFC). Rapidly and slowly degraded and undegraded in situ CP fractions and in vitro DM, organic matter, and NDF digestibility were determined on each TMR. The TMR were scanned using near-infrared reflectance spectroscopy (NIRS); spectra were retained with NIRS calibration and cross-validation statistics were determined using partial least squares regression methods. The CP, NDF, starch, in vitro DM, and in vitro indigestible NDF contents of TMR were predicted by NIRS with good degrees (R2 >0.85) of accuracy with proportionally low standard errors of prediction. Moderate utility of NIRS to predict the NFC (R2 = 0.83) and fat content (R2 = 0.81) of TMR was observed. Rapidly, slowly, and undegraded in situ CP fractions in TMR were not well predicted by NIRS. Similarly, soluble CP, NDF CP, total ethanol-soluble carbohydrate, and in vitro NDF digestibility (% of NDF) were not well predicted by NIRS. Ratios of nutrient range to reference laboratory method error were calculated and found to be positively related (R2 = 0.84) to NIRS predictability of a given TMR nutrient, suggesting some laboratory procedures were not precise enough to yield suitable NIRS predictions. Data suggest that NIRS has utility to predict basic nutrients such as CP, NDF, starch, NFC, and fat in TMR. However, difficulty was observed using NIRS in predicting key biological nutrients in TMR such as in situ CP fractions and in vitro NDF digestibility. Difficulty of NIRS in predicting these nutrients is related to the level of reference method error in relationship to the range of nutrient values in TMR, but other sources of prediction error may exist.

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Near-infrared evaluation of wet mink diets

Cited by 8 publications

References 12 publications

Feasibility of Diode-Array Instruments To Carry Near-Infrared Spectroscopy from Laboratory to Feed Process Control

Feasibility of Diode-Array Instruments To Carry Near-Infrared Spectroscopy from Laboratory to Feed Process Control

Near Infrared Reflectance Spectroscopy (NIRS) for determination of chemical entities of natural pasture from Ethiopia

Utility of Near-Infrared Reflectance Spectroscopy to Predict Nutrient Composition and In Vitro Digestibility of Total Mixed Rations

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