2 .双能x线吸收法测定鸡的加工体重;预测模型的验证

D.A. Martinez, J.T. Weil, N. Suesuttajit, A. Beitia , P. Maharjan , K. Hilton , C. Umberson, A. Scott, C.N. Coon
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引用次数: 7

摘要

双能x射线吸收测定法(DEXA)已被证明可以预测鸡肉胴体和切块的加工重量。本研究旨在验证先前开发的使用DEXA预测加工权重的模型。本试验以80只地面栏饲养的肉鸡为试验对象,随机饲喂5种饲粮处理中的一种。第41天称重,每栏随机选取7只,记录体重。停饲期结束后,每圈5只选定的鸟被运送到加工厂,其余的鸟被再次喂食。在冷冻一小时前后对胴体进行称重。将冷冻后的胴体切开,记录每一块商业肉的重量(胸片、鸡胸肉、鸡翅、鸡腿、总白肉和即食部分),并计算相应的未冷冻重量。在第43天或第44天,每个围栏选择的另外两只鸟称重,在不禁食的情况下进行dexa扫描,并通过应用先前开发的公式确定其禁食体重。通过将dexa报告的值输入到先前开发的一组模型中,可以获得预测的处理权重。所有数据均调整为相同体重标准。钢笔平均观察处理权值和dexa预测权值用于验证模型。计算预测值与实测值之间的线性回归,采用R2作为精度指标。测定不同饲粮处理的预测响应曲线与观测响应曲线的平行性,以及模型预测误差和精度。验证标准为验证R2、从开发到验证的R2变化量、响应曲线的平行度和预测精度。除机翼重量预测模型(预测误差 > 10 %)外,所有模型预测切块重量的验证R2均为 > 0.84,预测误差均为 ≤ 5.85 %。用dexa预测值和加工厂数据比较各处理的响应曲线,各性状均呈平行趋势。综上所述,除预测鸡翅重量的模型外,其余模型均满足评价标准并得到验证,支持DEXA法确定肉鸡加工体重,并将其应用于提高胸肉产量的营养干预研究。
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Processing weights of chickens determined by dual-energy X-ray absorptiometry: 3. Validation of prediction models

Dual-Energy X-ray Absorptiometry (DEXA) has been shown to predict the processing weights of the carcass and cut-up pieces of the chicken. This study aimed to validate models to predict processing weights using DEXA that were previously developed. An experiment was conducted with broilers grown up in 80 floor pens randomly subjected to one of five dietary treatments. On day 41, all birds were weighed, seven were randomly selected per pen, and their weights were recorded. After a feed withdrawal period, five selected birds per pen were transported to a processing plant, and the rest of the birds were fed again. The carcass was weighed before and after chilling for one hour. The chilled carcass was cut up, the weights of each commercial piece were recorded (breast fillet, tenders, wings, leg quarters, total white meat, and ready-to-cook parts), and the corresponding unchilled weights were calculated. On day 43 or 44, the other two birds selected per pen were weighed, DEXA-scanned without fasting, and their fasted weights were determined by applying a previously developed equation. Predicted processing weights were obtained by entering DEXA-reported values into a set of models previously developed. All the data were adjusted to the same BW basis. The pen mean observed processing weights and the DEXA-predicted ones were used to validate the models. The linear regression between predicted and observed values was calculated, and the R2 was used as a precision index. The parallelism of the predicted and observed response curves across dietary treatments, and the model prediction error and accuracy were determined. The validation criteria were based on the validation R2, the change in R2 from development to validation, the parallelism of response curves, and the prediction accuracy. The validation R2 of all tested models predicting the weight of cut-up pieces was > 0.84, and their prediction errors were ≤ 5.85 %, except for the model predicting the weight of the wings (prediction error > 10 %). All traits showed parallel trends when the response curves across treatments obtained with the DEXA-predicted values or the processing plant data were compared. In conclusion, all models but the one predicting the weight of wings satisfied the evaluation criteria and were validated, supporting the use of DEXA to determine the processing weights of broilers and its application to the study of nutrition interventions to improve breast meat production.

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Corrigendum to “The role of anti-E. coli antibody from maternal colostrum on the colonization of newborn dairy calves gut with Escherichia coli and the development of clinical diarrhea” [Animal Open Space 2 (2023) 100037] Method: Body composition assessment of sows using dual-energy X-ray absorptiometry Data paper: Dataset describing the effects of environmental enrichment and sows’ characteristics on the peripheral blood mononuclear cell transcriptome Method: Protocol for in-ovo stimulation with selected pro-/prophy-biotics to mitigate Campylobacter jejuni in broiler chickens Method: Standard operating procedure for the administration of swallowable devices to study pig’s gut content in a non-invasive way
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