A new procedure (LOCAL) for local calibrations is presented. LOCAL selects spectra from a library of samples and computes a PLS calibration equation for each constituent of the sample. This study evaluated the performances of LOCAL on the prediction of ground corn grain and haylage using several different combinations of data transformations, wavelength segment reduction, number of PLS factors and samples used in calibration. LOCAL resulted in lower SEP values for all the constituents of corn and dry matter of haylage with improvements ranging between 6 to 13%. Global calibrations had only a small advantage over LOCAL (1-2%) in the prediction of acid de-tergent fibre and crude protein in haylage. The two most important variables controlling the accuracy of predictions were number of samples in the calibration and number of PLS factors in the solution. Best results were obtained using 150 to 225 samples and more than 20 PLS factors per calibration equation. The speed of the LOCAL procedure is 0.5-2 s per sample on a 90 MHz computer. With this speed and accuracy, LOCAL is now available for real-time routine operation on a Windows platform.

Summary Subacute rumen acidosis (SARA) represents one of the most important metabolic disorders in intensive dairy farms that affects rumen fermentations, animal welfare, productivity and farm profitability. The aim of the present study was to study the occurrence of SARA in intensive Italian dairy herds and to determine the relationship between diet composition, ruminal pH and short chain fatty acids (SCFA) concentration. Ten commercial dairy herds were investigated; twelve cows in each herd were selected randomly among animal without clinical signs of disease, with good body condition and between 5 and 60 day‐in‐milk (DIM), to perform rumenocentesis and obtain rumen fluid. Ruminal pH was determined immediately after sampling and concentration of SCFA in ruminal fluid was determined on samples after storage. An other objective of this research was to study in detail the effects of rumenocentesis on animal health: this study could confirm the extreme validity of this technique as ruminal sampling. Results were subject to anova and correlation analysis using sigma stat 2.03. The results indicated the presence of SARA in three herds (more than 33% cows with rumen pH < 5.5), a critical situation (more than 33% cows with rumen pH < 5.8) in five farms and a normal rumen pH condition in two herds. In particular, dairy herds show on average SCFA concentration of 150, 145, 123 mmol/l for low pH, critical pH and normal pH herds respectively. There were not significant differences among diet composition even if herds with SARA showed a light discordance between initially chemistry composition and residual feed. In the affected herds it was not possible to understand the exact causes of SARA. Animal management seems to be one of the most important factors in developing SARA including total mixed ration preparation.

This study evaluated the use of an algorithm (LOCAL) for local calibration using multi-product databases. Four different databases were used: forages (hay, corn silage, haylage, small grain silage and total mixed ration; n=2924), grain (barley, corn, oats and wheat; n=1464), meat (meat and bone meal, fish meal and poultry meal; n=693) and feed (bakery products, mixed feed, poultry feed and soya products; n=1518). One-tenth of the samples were selected for validation from each database. Predictions of validation samples using generic and specific global calibrations were compared to the predictions generated by LOCAL. Standard errors of prediction for LOCAL calibrations were always lower than those of generic global calibrations and similar to those of specific global calibrations. However, LOCAL predictions were further improved by using different settings for each constituent. The analysis of the samples selected by LOCAL showed that for heterogeneous products such as total mixed rations and corn silage, LOCAL optimised predictions by choosing samples from different products. LOCAL calibration was then used with one database (n=6599) comprising all the samples. Standard errors of prediction were similar to those obtained with the four different databases. LOCAL can accurately predict the composition of different products using multi-product databases. Routine analysis can be simplified by using LOCAL calibration combined with large databases. In addition, LOCAL can provide accurate predictions of spectra from remote standardised instrument without the operator identifying the sample.

The objective of this study was to determine intake and site and extent of nutrient digestion of lactating cows grazing pasture with or without energy supplementation. Four dual-cannulated (rumen and proximal duodenum) cows were randomly assigned to two groups to graze mixed cool season grass legume pasture with either no supplement or with 6.4 kg of cracked corn and mineral mix daily in a switchback design with three 2-wk periods. Markers (Cr2O3 and Co-EDTA) were used to estimate intake, duodenal flow, fecal output, and fractional rates of passage from the rumen. Daily OM intake was similar between diets, but OM intake of pasture was lower when cows were fed corn. Apparent OM and NDF digestibilities in the rumen and total digestive tract were lower when cows were supplemented with corn than when they consumed pasture only. Supplemental corn decreased ruminal NH3 N (22 vs. 17 mg/dl) and increased N recovery at the duodenum (86% vs. 75% of N intake). Nonammonia, nonmicrobial N flowing to the duodenum was 67% of the total NAN flow. Corn increased energy intake of grazing cows, but decreased herbage intake and digestibility.

The objective of this study was to evaluate near-infrared reflectance spectroscopy (NIRS) as a tool to predict the physicochemical composition of breast meat samples of laying hens fed 4 different diets, a control and 3 diets enriched with different sources of n-3 polyunsaturated fatty acids: marine origin, extruded linseed, and ground linseed. Furthermore, NIRS was used as a tool to classify meat samples according to feeding regimen. Samples were analyzed chemically for DM, ash, protein, lipids, and fatty acid profile. Absorption spectra were collected in diffuse reflectance mode between 1,100 and 2,498 nm every 2 nm. The calibration results for the 72 meat samples were accurate in predicting DM, protein, lipids, and major fatty acids. Poor results were obtained for the calibration equations for ash, pH, color, and lipid oxidation parameters. Partial least squares discriminant analysis was developed to differentiate the breast meat samples that originated from hens fed the different diets. The performance of the discriminant models showed 100% correct classification between the control and the enriched diets. It was concluded that NIRS could be used for quality control predicting chemical composition of poultry meat and possibly some dietary treatments applied to the chickens.