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Quality refers to the characteristics of products that meet the demands and expectations of the end users. Beef quality is a convergence between product characteristics on one hand and consumers’ experiences and demands on the other. This paper reviews the formation of consumer beef quality perception, the main factors determining beef sensory quality, and how to measure and predict beef eating quality at scientific and industrial levels. Beef quality is of paramount importance to consumers since consumer perception of quality determines the decision to purchase and repeat the purchase. Consumer perception of beef quality undergoes a multi-step process at the time of purchase and consumption in order to achieve an overall value assessment. Beef quality perception is determined by a set of quality attributes, including intrinsic (appearance, safety, technological, sensory and nutritional characteristics, convenience) and extrinsic (price, image, livestock farming systems, commercial strategy, etc.) quality traits. The beef eating qualities that are the most valued by consumers are highly variable and depend mainly on the composition and characteristics of the original muscle and the post-mortem processes involved in the conversion of muscle into meat, the mechanisms of which are summarized in this review. Furthermore, in order to guarantee good quality beef for consumers in advance, the prediction of beef quality by combining different traits in scenarios where the animal, carcass, and muscle cuts can be evaluated is also discussed in the current review.

Grain quality is one of the key targets to be improved for rice breeders and covers cooking, eating, nutritional, appearance, milling, and sensory properties. Cooking and eating quality are mostly of concern to consumers and mainly determined by starch structure and composition. Although many starch synthesis enzymes have been identified and starch synthesis system has been established for a long time, novel functions of some starch synthesis genes have continually been found, and many important regulatory factors for seed development and grain quality control have recently been identified. Here, we summarize the progress in this field as comprehensively as possible and hopefully reveal some underlying molecular mechanisms controlling eating quality in rice. The regulatory network of amylose content (AC) determination is emphasized, as AC is the most important index for rice eating quality (REQ). Moreover, the regulatory mechanism of REQ, especially AC influenced by high temperature which is concerned as a most harmful environmental factor during grain filling is highlighted in this review.

Good eating quality is a highly desirable trait of rice which determines its commercial value and market share. However, the molecular basis of this trait remains largely unknown. Here, three high-quality conventional rice cultivars, including two superior eating quality cultivars Meixiangzhan-2 (MXZ) and Xiangyaxiangzhan (XYXZ), and one ordinary eating quality cultivar Huanghuazhan (HHZ), were analyzed by comparative metabolomics to identify the inherent mechanism for the formation of superior eating quality. The results showed 58.8% of common differential substances between MXZ vs HHZ and XYXZ vs HHZ were enriched in MXZ and XYXZ, whereas 39.2% of them were prominently decreased compared with HHZ, mainly including amino acids, carbohydrates, lipids, phenolamides, and flavonoids, which may be the primary factors leading to the differences of taste and flavor among these three cultivars. We also found that lysine derivatives and fatty acids may have a close relationship with taste. These results above provide important insights into the taste-forming mechanism of rice and will be beneficial for superior eating quality rice breeding.

A simple index that reflects the potential eating quality of beef carcasses is very important for producer feedback. The Meat Standards Australia (MSA) Index reflects variation in carcass quality due to factors that are influenced by producers (hot carcass weight, rib fat depth, hump height, marbling and ossification scores along with milk fed veal category, direct or saleyard consignment, hormonal growth promotant status and sex). In addition, processor impacts on meat quality are standardised so that the MSA Index could be compared across time, breed and geographical regions. Hence, the MSA Index was calculated using achilles hung carcasses, aged for 5 days postmortem. Muscle pH can be impacted by production, transport, lairage or processing factors, hence the MSA Index assumes a constant pH of 5.6 and loin temperature of 7oC for all carcasses. To quantify the cut weight distribution of the 39 MSA cuts in the carcass, 40 Angus steers were sourced from the low (n=13), high (n=15) and myostatin (n=12) muscling selection lines. The left side of each carcass was processed down to the 39 trimmed MSA cuts. There was no difference in MSA cut distribution between the low and high muscling lines (P>0.05), although there were differences with nine cuts from the myostatin line (P<0.05). There was no difference in the MSA Index calculated using actual muscle percentages and using the average from the low and high muscling lines (R 2=0.99). Different cooking methods impacted via a constant offset between eating quality and carcass input traits (R 2=1). The MSA Index calculated for the four most commercially important cuts was highly related to the index calculated using all 39 MSA cuts (R 2=0.98), whilst the accuracy was lower for an index calculated using the striploin (R 2=0.82). Therefore, the MSA Index was calculated as the sum of the 39 eating quality scores predicted at 5 days ageing, based on their most common cooking method, weighted by the proportions of the individual cut relative to total weight of all cuts. The MSA Index provides producers with a tool to assess the impact of management and genetic changes on the predicted eating quality of the carcass. The MSA Index could also be utilised for benchmarking and to track eating quality trends at farm, supply chain, regional, state or national levels.

This paper reviews recent research into predicting the eating qualities of beef. A range of instrumental and grading approaches have been discussed, highlighting implications for the European beef industry. Studies incorporating a number of instrumental and spectroscopic techniques illustrate the potential for online systems to non-destructively measure muscle pH, colour, fat and moisture content of beef with R 2 (coefficient of determination) values >0.90. Direct predictions of eating quality (tenderness, flavour, juiciness) and fatty acid content using these methods are also discussed though success is greatly variable. R 2 values for instrumental measures of tenderness have been quoted as high as 0.85 though R 2 values for sensory tenderness values can be as low as 0.01. Discriminant analysis models can improve prediction of variables such as pH and shear force, correctly classifying beef samples into categorical groups with >90% accuracy. Prediction of beef flavour continues to challenge researchers and the industry alike, with R 2 values rarely quoted above 0.50, regardless of instrumental or statistical analysis used. Beef grading systems such as EUROP and United States Department of Agriculture systems provide carcase classification and some indication of yield. Other systems attempt to classify the whole carcase according to expected eating quality. These are being supplemented by schemes such as Meat Standards Australia (MSA), based on consumer satisfaction for individual cuts. In Australia, MSA has grown steadily since its inception generating a 10% premium for the beef industry in 2015-16 of $187 million. There is evidence that European consumers would respond to an eating quality guarantee provided it is simple and independently controlled. A European beef quality assurance system might encompass environmental and nutritional measures as well as eating quality and would need to be profitable, simple, effective and sufficiently flexible to allow companies to develop their own brands.

Rice ( L.) is an important cereal that provides food for more than half of the world's population. Besides grain yield, improving grain quality is also essential to rice breeders. Amylose content (AC), gelatinization temperature (GT) and gel consistency (GC) are considered to be three indicators for cooking and eating quality in rice. Using a genetic map of RILs derived from the super rice Liang-You-Pei-Jiu with high-density SNPs, we detected 3 QTLs for AC, 3 QTLs for GT, and 8 QTLs for GC on chromosomes 3, 4, 5, 6, 10, and 12. locus, an important determinator for AC and GC, resided in one QTL cluster for AC and GC, and here. And a novel major QTL on chromosome 10 was identified in both Lingshui and Hangzhou. With the BC F population derived from a CSSL harboring the segment for from 93-11 in PA64s background, it was fine mapped between two molecular markers within 181 kb region with 27 annotated genes. Quantitative real-time PCR results showed that eight genes were differentially expressed in endosperm of two parents. After DNA sequencing, only , which encodes a F-box domain containing protein, has 2 bp deletion in the exon of PA64s, resulting in a premature stop codon. Therefore, is considered to be the most likely candidate gene for associated with gel consistency. Identification of provides a new genetic resource for improvement of rice quality.

Differences in cooked rice and starch and protein physicochemical properties of three japonica rice were compared systematically. Cultivars of japonica rice, Daohuaxiang2, from Northeast China (NR) and two semiglutinous japonica rice (SGJR), Nangeng46 and Nangeng2728, from the Yangtze River Delta (YRD) were investigated. Both Daohuaxiang2 and Nangeng46 achieved high taste values, but there were great differences in starch and protein physicochemical properties. Daohuaxiang2 showed higher apparent amylose content (AAC), lower protein content (PC), and longer amylopectin (especially fb2 and fb3) and amylose chain lengths, resulting in thicker starch lamellae and larger starch granule size. Its cooked rice absorbed more water and expanded to larger sizes. All of these differences created a more compact gel network and harder but more elastic cooked rice for Daohuaxiang2. Nangeng46 produced a lower AAC, a higher PC, shorter amylopectin and amylose chain lengths, thinner starch lamellae, and smaller starch granule sizes, creating a looser gel network and softer cooked rice. The two SGJR, Nangeng46 and Nangeng2728, had similar low AACs but great differences in taste values. The better-tasting Nangeng46 had a lower PC (especially glutelin content) and higher proportion of amylopectin fa chains, which likely reduced the hardness, improved the appearance, and increased the adhesiveness of its cooked rice. Overall, both types of japonica rice from the NR and YRD could potentially have good eating qualities where Nangeng46’s cooked rice was comparable to that of Daohuaxiang2 because of its lower AC. Moreover, its lower PC and higher proportion of amylopectin fa chains likely improved its eating quality over the inferior-tasting SGJR, Nangeng2728. This research lays a foundation for the improvement of the taste of japonica rice in rice breeding.

The Meat Standards Australia (MSA) grading scheme has the ability to predict beef eating quality for each ‘cut×cooking method combination’ from animal and carcass traits such as sex, age, breed, marbling, hot carcass weight and fatness, ageing time, etc. Following MSA testing protocols, a total of 22 different muscles, cooked by four different cooking methods and to three different degrees of doneness, were tasted by over 19 000 consumers from Northern Ireland, Poland, Ireland, France and Australia. Consumers scored the sensory characteristics (tenderness, flavor liking, juiciness and overall liking) and then allocated samples to one of four quality grades: unsatisfactory, good-every-day, better-than-every-day and premium. We observed that 26% of the beef was unsatisfactory. As previously reported, 68% of samples were allocated to the correct quality grades using the MSA grading scheme. Furthermore, only 7% of the beef unsatisfactory to consumers was misclassified as acceptable. Overall, we concluded that an MSA-like grading scheme could be used to predict beef eating quality and hence underpin commercial brands or labels in a number of European countries, and possibly the whole of Europe. In addition, such an eating quality guarantee system may allow the implementation of an MSA genetic index to improve eating quality through genetics as well as through management. Finally, such an eating quality guarantee system is likely to generate economic benefits to be shared along the beef supply chain from farmers to retailors, as consumers are willing to pay more for a better quality product.

The raw eating quality of sweet potato is complex. As consumers start paying more attention to the raw eating quality of tuberous roots in sweet potato, the evaluation of the raw eating quality of sweet potato is becoming an important issue. Therefore, we measured 16 quality indicators in 81 varieties of sweet potato. It was found that these 16 quality traits had different coefficients of variation (C.V.). Among them, the C.V. of fructose, glucose, and adhesiveness were the largest: 87.95%, 87.43% and 55.09%, respectively. The cluster analysis method was used to define six categories of the different tuberous roots of sweet potato. Group I, III, and IV had a stronger hardness and higher starch and cellulose content. Groups II, V, and VI were softer, with a high moisture and soluble sugar content. The principal component analysis method was used to comprehensively evaluate 16 quality indicators of 81 sweet potato varieties. It was found that Futian1, Taishu14, and Nanshu022 are good varieties in terms of raw eating quality. These varieties have low hardness, high adhesiveness in texture, high soluble sugar content, and low starch and cellulose. Future research should focus on improving the raw eating quality of sweet potato by reducing hardness, starch, and cellulose, while increasing adhesiveness, soluble sugar, and moisture content.