Quality Grade Research Articles

A sensor array based on a nanozyme with polyphenol oxidase activity for the identification of tea polyphenols and Chinese green tea

Green tea is popular among consumers because of its high nutritional value and unique flavor. There is often a strong correlation among the type of tea, its quality level and the price. Therefore, the rapid identification of tea types and the judgment of tea quality grades are particularly important. In this work, a novel sensor array based on nanozyme with polyphenol oxidase (PPO) activity is proposed for the identification of tea polyphenols (TPs) and Chinese green tea. The absorption spectra changes of the nanozyme and its substrate in the presence of different TPs were first investigated. The feature spectra were scientifically selected using genetic algorithm (GA), and then a sensor array with 15 sensing units (5 wavelengths × 3 time) was constructed. Combined with the support vector machine (SVM) discriminative model, the discriminative rate of this sensor array was 100% for different concentrations of typical TPs in Chinese green tea with a detection limit of 5 μM. In addition, the identification of different concentrations of the same tea polyphenols and mixed tea polyphenols have also been achieved. Based on the above study, we further developed a facile and efficient new method for the category differentiation and adulteration identification of green tea, and the accuracy of this array was 96.88% and 100% for eight types of green teas and different adulteration ratios of Biluochun, respectively. This work has significance for the rapid discrimination of green tea brands and adulteration.

Durian Species Classification Using Deep Learning Method

Durian is a popular fruit in Southeast Asia, and the market offers various species of durians. Accurate species classification is crucial for quality control, grading, and marketing. However, the complexity of this task has led to the utilization of machine learning and deep learning methods. Traditional machine learning algorithms, such as K-Nearest Neighbors, Linear Discriminant Analysis, Support Vector Machines, and Random Forests, have demonstrated good accuracy, but they require extensive feature engineering. Deep learning algorithms, particularly Convolutional Neural Networks, can automatically extract features, making them less dependent on manual feature selection. This research aims to review deep learning classification algorithms, including Convolutional Neural Networks and Recurrent Neural Networks, to determine the most suitable algorithm for an efficient and accurate durian classification system. The objective is to enhance the precision and speed of durian species classification, presenting potential advantages for both durian producers and consumers. The literature review revealed that Convolutional Neural Networks outperformed other deep learning and traditional machine learning algorithms on datasets of varying sizes, achieving the highest accuracy of 98.96% through techniques like image resizing, color conversion, and additional parameters such as days harvested and dry weight. Deep learning emerges as a promising approach for robust and accurate durian species recognition, with future directions including developing models to classify durian species from different plant parts and even real-time video analysis. However, while Convolutional Neural Networks lead the way, a critical research gap exists in identifying optimal features, necessitating further investigation to refine durian species recognition accuracy.

Comparison of Standard Calendar GA4+7 Plant Growth Regulator Programs to Applications Timed to Rainfall Events for Management of Scarf Skin, a Fruit Finish Disorder of Apple

Scarf skin is a fruit finish disorder of apple that leads to reduced grade quality and rejected crop loads. Although not established as a disorder specific to certain cultivars, the manifestation of the disorder is more apparent on red-skinned cultivars such as Gala and Stayman at harvest, where its whiteish cast is held in clear contrast with the redness of the mature fruit. The disorder afflicts fruit during the first 40 days after petal fall as the fruitlet is developing, although symptoms of the disorder may not be clearly visible until many months after. The exact causes of apple scarf skin are not well defined, although it is known that the presence of the disorder can be influenced by environment, fruit size, and some pesticides. Management currently relies on four applications of the plant hormones gibberellins A4 and A7 (GA4+7) during the first 40 days after bloom. Here, we describe our attempts to use a commercialized GA4+7 product (ProVide 10SG) starting at petal fall, with applications made after or before rainfall events, to ease rainfall's putative influence on scarf skin development. We compared our programs to the standard program over 4 years in research orchards and a commercial orchard but observed no consistent reduction in scarf skin severity or incidence of out-of-grade fruit, in part due to the year-to-year variability in the manifestation of scarf skin. Rainfall, at the thresholds defined, does not appear to be a sufficiently significant factor influencing scarf skin development such that a GA4+7 management program can be designed. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Non-destructive identification of Pseudostellaria heterophylla from different geographical origins by Vis/NIR and SWIR hyperspectral imaging techniques.

The composition of Pseudostellaria heterophylla (Tai-Zi-Shen, TZS) is greatly influenced by the growing area of the plants, making it significant to distinguish the origins of TZS. However, traditional methods for TZS origin identification are time-consuming, laborious, and destructive. To address this, two or three TZS accessions were selected from four different regions of China, with each of these resources including distinct quality grades of TZS samples. The visible near-infrared (Vis/NIR) and short-wave infrared (SWIR) hyperspectral information from these samples were then collected. Fast and high-precision methods to identify the origins of TZS were developed by combining various preprocessing algorithms, feature band extraction algorithms (CARS and SPA), traditional two-stage machine learning classifiers (PLS-DA, SVM, and RF), and an end-to-end deep learning classifier (DCNN). Specifically, SWIR hyperspectral information outperformed Vis/NIR hyperspectral information in detecting geographic origins of TZS. The SPA algorithm proved particularly effective in extracting SWIR information that was highly correlated with the origins of TZS. The corresponding FD-SPA-SVM model reduced the number of bands by 77.2% and improved the model accuracy from 97.6% to 98.1% compared to the full-band FD-SVM model. Overall, two sets of fast and high-precision models, SWIR-FD-SPA-SVM and SWIR-FD-DCNN, were established, achieving accuracies of 98.1% and 98.7% respectively. This work provides a potentially efficient alternative for rapidly detecting the origins of TZS during actual production.

Improved deep belief network for estimating mango quality indices and grading: A computer vision-based neutrosophic approach

ABSTRACT This research introduces a revolutionary machinet learning algorithm-based quality estimation and grading system. The suggested work is divided into four main parts: Ppre-processing, neutroscopic model transformation, Feature Extraction, and Grading. The raw images are first pre-processed by following five major stages: read, resize, noise removal, contrast enhancement via CLAHE, and Smoothing via filtering. The pre-processed images are then converted into a neutrosophic domain for more effective mango grading. The image is processed under a new Geometric Mean based neutrosophic approach to transforming it into the neutrosophic domain. Finally, the prediction of TSS for the different chilling conditions is done by Improved Deep Belief Network (IDBN) and based on this; the grading of mango is done automatically as the model is already trained with it. Here, the prediction of TSS is carried out under the consideration of SSC, firmness, and TAC. A comparison between the proposed and traditional methods is carried out to confirm the efficacy of various metrics.

Ocular surface parameter changes in the untreated fellow eye after unilateral cataract surgery with short-term administration of anti-inflammatory eye drops

This study aimed to investigate the changes in clinical parameters of dry eye disease and meibomian gland dysfunction in both the operated and untreated fellow eyes of patients who underwent unilateral cataract surgery with the short-term administration of anti-inflammatory eye drops in the surgical eye. The medical charts of 57 consecutive patients who underwent unilateral cataract surgery and received 1% prednisolone acetate and non-steroidal anti-inflammatory drug (NSAID, 0.1% bromfenac sodium) eye drops were reviewed. The preoperative ocular surface disease index questionnaire score (38.9 ± 20.5) decreased significantly to 15.2 ± 16.4 at post-surgical 1 week and further to 12.8 ± 11.4 after 1 month. Although meibum quality grade increased and corneal sensitivity decreased at 1 week in operated eyes, corneal erosion scores and Sjogren’s International Collaborative Clinical Alliance ocular staining scores even improved over a month in the untreated fellow eyes. The tear matrix metalloproteinase (MMP)-9 grade decreased in both operated eyes and untreated fellow eyes after 1 month from surgery. In conclusion, the short-term topical anti-inflammatory treatment using steroid and NSAID eye drops in the operated eye after cataract surgery decreased subjective ocular surface discomfort and improved ocular surface staining scores and tear MMP-9 expression in the untreated fellow eyes.

Clinical tools for evaluating the severity of overactive bladder: A systematic review of psychometric properties

Objectives To systematically evaluate the evidence describing the psychometric properties of clinical measures for assessing overactive bladder symptoms (urinary urgency with or without urge urinary incontinence, urinary frequency and nocturia). To evaluate the quality of this evidence-base using the COnsensus-based Standards for selecting health status Measurement INstruments (COSMIN) checklist and the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) tools. Data sources Five electronic databases (CINAHL, EMBASE, MEDLINE, Scopus and Web of Science) were searched from dataset inception to August 2023. Review methods Study screening, data extraction and quality appraisal were performed by two independent authors. Inclusion criteria were studies testing one or more psychometric properties of clinical tools for the assessment of overactive bladder symptoms among adults aged 18 years and older for both sexes. The methodological quality and quality of the evidence were evaluated using the COSMIN checklist and GRADE tools, respectively. Results The search identified 40 studies totalling 10,634 participants evaluating the psychometric properties of 15 clinical tools. The COSMIN methodological quality was rated good for most measures, and the GRADE quality of evidence ranged from low (13%) to high (33%). The Overactive Bladder Symptom Score, Overactive Bladder Questionnaire and Neurogenic Bladder Symptom Score were of good methodological and high-GRADE evidence qualities. Conclusion Overactive Bladder Symptom Score, the Overactive Bladder Questionnaire and the Neurogenic Bladder Symptoms Score are promising psychometrically sound measures. The Overactive Bladder Symptom Score has been applied to the most culturally diverse populations supported by studies of good methodological and high-GRADE evidence quality.

Causal Artificial Intelligence Models of Food Quality Data.

Causal Artificial Intelligence Models of Food Quality Data.

Using Computer Vision for Mango Price Estimation Based on Breed Classification and Quality Grading

Using Computer Vision for Mango Price Estimation Based on Breed Classification and Quality Grading

Application of lightweight YOLOv5 for walnut kernel grade classification and endogenous foreign body detection

The appearance quality grading of walnut kernels is extremely important in the deep processing of walnuts. Foreign body pollution in food is also a challenging problem that has been troubling food companies. Therefore, it is necessary to propose an efficient and non-destructive detection method for walnut kernels and their endogenous foreign bodies. To improve the quality and safety of walnut deep-processed products, this paper proposes an improved detection model based on the original YOLO V5. The improved model achieves a reduction in memory consumption by 38% and computational workload by 34%. Additionally, it increases the average precision of the model by 1.1% and improves CPU speed by 52%. When compared to other lightweight networks, our model demonstrates optimal overall performance. Moreover, our algorithm significantly enhances the precision of detecting small targets and target selection, as compared to the original model.By reducing the input image size of the model, it is able to meet the requirements of real-time detection of walnut kernels and their endogenous foreign bodies on the edge device Raspberry Pi. This provides a technical reference for the non-destructive detection of food quality and its endogenous foreign bodies.

Dose effects of encapsulated butyric acid and zinc on beef feedlot steer growth performance, dietary net energy utilization, rumen morphometrics, small intestine histology, and carcass characteristics.

The objective of this study was to determine the effects that increasing doses of encapsulated butyric acid and zinc (BZ) have on feedlot steer growth performance, rumen morphometrics and small intestine histology (data not statistically analyzed), dietary net energy utilization, and carcass characteristics. Steers [n = 272; shrunk body weight (BW) = 360 kg ± 74 kg] were assigned to dietary treatments [0 (CON), 1, 2, or 3g BZ/kg diet dry matter] in a randomized complete block design (RCBD) with pen (n = 32 total; n = 8 per treatment) as experimental unit. Pens were blocked by cattle source and location within the feedyard. Cattle were fed until visually assessed to have 1.27cm rib-fat and were shipped for harvest at a commercial beef abattoir. Carcass and liver health data were recorded. A subset of steers (n = 8 total; n = 2 per treatment) was harvested at the SDSU Meat Laboratory to collect empty body measurements, rumen samples for morphometric analysis, and duodenal and ileal samples for histological analysis to provide context to feeding trial outcomes. Feedlot growth performance data was calculated on a carcass-adjusted basis: hot carcass weight (HCW)/0.625. Data were analyzed as a RCBD with fixed effects of BZ inclusion level and block was considered a random effect; pre-planned contrasts for CON vs. BZ, plus linear, and quadratic responses were tested. No differences (P ≥ 0.11) were observed for final BW, dry matter intake, average daily gain (ADG), feed conversion efficiency (G:F), performance calculated dietary net energy, HCW, ribeye area, rib-fat thickness, marbling score, estimated empty body fat, or distribution of USDA yield grade (YG) 1, 3, 4, 5, and USDA quality grade among treatments. A tendency (P = 0.10) was observed for CON vs. BZ for calculated YG. Tendencies were detected for USDA YG 2 carcass distribution (linear; P = 0.07) and for normal and abscessed liver prevalence (quadratic; P = 0.08). Dressed yield tended to be greater (P = 0.08) for BZ vs. CON and increased with dose (linear; P = 0.05). Receiving period shrunk BW, ADG, and G:F was improved (P ≤ 0.02) for BZ-supplemented steers compared to CON. Data from this study suggests that the addition of BZ to feedlot finishing diets to improve receiving period growth performance and decreasing the prevalence of abscessed livers should be further investigated.

Potted Phalaenopsis Grading: Precise Bloom and Bud Counting with the PA-YOLO Algorithm and Multiviewpoint Imaging

The accurate detection and counting of flowers ensure the grading quality of the ornamental plants. In automated potted flower grading scenarios, low detection precision, occlusions and overlaps impact counting accuracy. This study proposed a counting method combining a deep learning algorithm with multiple viewpoints. Firstly, a flower detection model, PA-YOLO, was developed based on YOLOv5 by designing a two-scale detection branch, optimizing the number of bottlenecks and integrating a dynamic head framework. Next, PA-YOLO was used to detect grouped 360-viewpoint images of each potted plant to determine the optimal number of viewpoints for counting. The detection results indicated that PA-YOLO achieved a mean average precision (mAP) of 95.4% and an average precision (AP) of 91.9% for occluded blooms on our Phalaenopsis flower dataset. For the optimal number of viewpoints, the average counting accuracy of buds and blooms was highest at three viewpoints, with scores of 96.25% and 93.33%, respectively. The final counting accuracy reached 95.56% in flower counting tests conducted from three viewpoints. The overall results suggest that the proposed method can effectively detect and count flowers in complex occlusion and overlap environments, providing guidance for designing and implementing the vision component in an automated potted flower grading system.

Design, Prototyping, and Evaluation of A New Machine Vision-Based Automated Sweetpotato Grading and Sorting System

HighlightsA computer algorithm pipeline with YOLOv8 was developed for grading sweetpotatoes for size and surface defects.Pneumatically powered sorting mechanisms were prototyped and evaluated for sweetpotato sorting.The sorting mechanisms achieved 96.9% accuracy and 92.5% repeatability at a conveyor speed of 12 cm s-1.The integrated system achieved online, automated grading and sorting accuracies of 93.8% and 92.7%, respectively.Abstract. Automated quality grading and sorting of fresh market sweetpotato roots have not been achieved at packing facilities, which consequently requires significant labor to manually grade the products and sort them by hand. Given an increasing shortage of labor and rising labor costs, there is a pressing need to develop automated grading and sorting technology for sweetpotato packers to stay competitive and profitable. This study reports on the design, prototyping, and evaluation of an innovative machine vision-based, automated sweetpotato grading and sorting system empowered by state-of-the-art artificial intelligence. The developed prototype consists of a custom-designed roller conveyor-based machine vision unit that performs size and surface defect detection and grading of sweetpotatoes traveling and rotating on the conveyor and pneumatically powered sorting mechanisms for automated sorting of the sweetpotatoes into three grades after they exit from the vision area. A computer algorithm pipeline was developed to segment and track individual sweetpotato instances by YOLOv8l in real-time and perform quality grading based on multiple views of samples for size (length and width) and surface defects, and then activate the sorting mechanisms to selectively segregate graded sweetpotatoes online. Experiments were conducted on the performance of the sorting mechanisms, the machine vision unit, and the fully integrated system at varied conveyor speeds. The sorter without vision integration achieved an accuracy of 96.9% and repeatability of 92.5% at a conveyor speed of 12 cm s-1. The machine vision system yielded an overall accuracy of 95.8% and 93.8% in grading three-class sweetpotatoes for surface defects alone and for both size and surface defects, respectively, at the conveyor speed of 12 cm s-1. An overall sorting accuracy of 92.7% was achieved by the integrated system at the conveyor speed of 12 cm s-1. Grading and sorting accuracies tended to decrease as the conveyor speed increased. A video demo of automated sweetpotato grading and sorting processes is available at https://youtu.be/3QEp9wFTiu0. With more dedicated efforts on hardware and software improvements, the developed grading and sorting machine prototype promises to evolve into a practical implementation at commercial sweetpotato packing facilities. Keywords: Artificial intelligence, Automated grading and sorting, Machine vision, Sweetpotato, System integration.

Remanufacturing of end-of-life laptop based on remaining useful life prediction and quality grading with random forest and cluster analysis

Remanufacturing of end-of-life laptop based on remaining useful life prediction and quality grading with random forest and cluster analysis

Quality detection and grading of peach fruit based on image processing method and neural networks in agricultural industry.

The grading of products is important in many ways. One of the important activities after harvesting agricultural products is product grading based on shape and color dimensions. This activity in the agricultural transformation industries, Bas Controller, improves various processes on fruits and vegetables with the same dimensions, which improves the storage conditions of the product, creates added value for the farmer, and gives the consumer the power to choose. The main focus of this study is the application of image processing in the field of identification and classification of fruits. It is an application that has received much less attention than other applications of image processing. The proposed systems presented in this article, are software solutions based on image processing techniques, including histogram matching techniques, for detection, Sable edge detection algorithms, Private edge and Kenny edge, Otsu threshold limit, and clustering method It is an automatic mean and classification of different degrees of fruit. In addition, it has been mentioned more about the examination and description of product grading and clustering methods, that by using the proposed application hardware and its connection with the software, a big step can be taken in product quality grading. This method can be used in product classification and packaging. The accuracy rate for peaches, lemons, apples, and tomatoes is 94.58%, 88.23%, 70%, and 93.33%, respectively. The best accuracy for all 20 sample levels is for peach fruit.

Live and carcass production traits for progeny of an F1 USDA Prime-Yield Grade 1 carcass clone sire compared to progeny of popular beef terminal sires.

The cloning of beef carcasses that grade United States Department of Agriculture (USDA)Prime-yield grade (YG) 1 (P1) has produced a sire that ranked well against high-performing bulls from multiple breeds. An F1 (P1 × P1 - first generation offspring) sire would ideally outperform its high-performing parents. A terminal sire study was conducted comparing progeny of an F1 (P1 × P1) sire (AxG1) against progeny (heifers and steers) of four high-performing sires of varying breeds {P1 (ALPHA); Angus; Simmental; Angus × Simmental}. Production traits included morbidity and mortality frequencies, weaning weight, feedlot arrival weight, and days on feed; carcass traits included frequency of abscessed liver and lung health, quality grade and YG parameters, total carcass value (US$), and carcass value per hundredweight (CWT [45.4 kg]; US$). A completely randomized experimental design was used; data were analyzed using a mixed model with a fixed effect of sire and random effects of harvest date, sex, and pen. AxG1 sired heifers had the highest (P < 0.01) marbling score, the highest (P < 0.01) carcass value per CWT, and numerically had the lowest calculated YG and highest frequency of YG one carcass. Steers sired by AxG1 had the least (P = 0.05) backfat, lowest (P < 0.01) calculated YG, highest (P < 0.01) marbling score, highest (P < 0.01) frequency of USDA Prime carcasses, the greatest (P < 0.03) total carcass value, and greatest (P < 0.01) carcass value per CWT. Collectively, AxG1 steer and heifer carcasses exhibited the least 12th rib fat thickness and lowest USDA YG in addition to the largest longissimus muscle area, highest marbling score, and greatest frequency of USDA Prime. These data suggest that AxG1 performed comparably to other high-performing industry terminal sires in carcass quality and YG outcomes.

The effect of a direct-fed microbial (10-G) on live animal performance, carcass characteristics, and Salmonella prevalence of fed beef heifers.

The objective of this study was to determine the efficacy of the direct-fed microbial 10-G upon cattle growth performance, liver and lung health, carcass quality, and yield outcomes, as well as prevalence and enumeration of Salmonella in feces and lymph nodes. Fed beef heifers (N = 1,400; initial shrunk body weight [BW] 343.3 ± 36.2kg) were blocked by the day of arrival and randomly allocated to one of two treatments (0 [negative control, CON] or 2g of a direct-fed microbial [10-G] that provided 1 billion CFUs per animal per day of Lactobacillus acidophilus, Enterococcus faecium, Pediococcus pentosaceus, L. brevis, and L. plantarum) with 10 pens per treatment. Recto-anal mucosal fecal samples (RAMs; n = 477) and subiliac lymph nodes (SLNs; n = 479) were collected longitudinally at harvest from 23 to 25 heifers per pen. Data were analyzed using mixed models; pen served as the experimental unit; block and harvest date were random effects. No differences were detected in dry matter intake (P = 0.78), final BW (P = 0.64), average daily gain (P = 0.51), gain to feed (P = 0.71), hot carcass weight (P = 0.54), dressed carcass yield (P = 0.52), 12th rib fat depth (P = 0.13), longissimus muscle area (P = 0.62), calculated empty body fat (P = 0.26), or marbling score (P = 0.82). Distributions of liver scores (P ≥ 0.34), quality grades (P ≥ 0.23), and yield grades (P ≥ 0.11) were also not different between treatments. A tendency was detected for more normal lungs (P = 0.08; 10-G = 65.96%, CON = 61.12%) and fewer inflated lungs at harvest for cattle fed 10-G (P = 0.10; 10-G = 0.29%, CON = 1.16%); other lung outcomes did not differ (P ≥ 0.54). Salmonella prevalence did not differ for RAM samples (P = 0.41; 10-G = 97.74%, CON = 96.82%) or SLN (P = 0.22; 10-G = 17.92%, CON = 13.66%). Salmonella concentration of RAM samples (P = 0.25; 10-G = 3.87 log CFU/g, CON = 3.32 log CFU/g) or SLN (P = 0.37; 10-G = 1.46 log CFU/g, CON = 1.14 log CFU/g) also did not differ between treatments at harvest. These results do not demonstrate any difference in live animal performance, carcass characteristics, or Salmonella carriage for heifers fed 10-G.

Data Mining for Early Cycle Life Prediction in Lithium-Ion Battery Production

The efficient prediction of product quality is a major challenge in lithium-ion battery production, as conventional measures such as aging are time-consuming and costly. This study presents a comprehensive data mining approach to predict the quality of lithium-ion batteries using linear and non-linear support vector machines. A methodology for extracting and selecting features from data sources within production is presented, and several feature selection algorithms – as well as models – are compared with regard to their predictive power. A minimum test error of 8.8 % for the early cycle life prediction was achieved, along with a classification accuracy of 96.6 %, when dividing the lithium-ion batteries into two quality grades with high and low cycle life.

An analysis of the influence of preslaughter management factors on welfare and meat quality outcomes in fed beef cattle in the United States.

During the preslaughter phase, cattle are transported from their place of origin to a slaughter facility, experiencing transportation, lairage, environmental factors, and novel environments. Although research exists that has focused how the preslaughter phase impacts cattle welfare and meat quality, some significant preslaughter management factors and subsequent welfare and meat quality outcomes have not been thoroughly explored. The objective of this study was to assess the effects of preslaughter management factors on welfare and meat quality outcomes in fed beef cattle in the United States. Transportation factors, environmental characteristics, lairage factors, cattle characteristics, and several meat quality variables were collected from 5 federally inspected commercial processing facilities in the United States. After excluding slaughter lots that included <75% complete datasets, a total of 619 slaughter lots representing 84,508 head of cattle were used for further analysis. Predictor variables of interest included processing plant, cattle breed, sex class, operation shift at the plant, distance traveled to the plant, truck waiting time to unload at the plant, lairage duration and space allowance, temperature humidity index, and windspeed. Outcome variables of interest included cattle mobility, carcass bruising, dark cutting (DC), quality grades, and hot carcass weights. Logistic and linear regressions were used to analyze the associations between the predictor and outcome variables of interest. Increased distance traveled and truck waiting time were associated with higher odds of mobility impairment (P = 0.0009 and P = 0.007, respectively), with each 10 km increase in distance traveled having an odds ratio (OR) of 1.001 (95% confidence interval [CI]: 1.000 to 1.001) and each 1-min increase in waiting time having an OR of 1.003 (CI: 1.001 to 1.004). Conversely, a 10-km increase in distance traveled decreased the odds of carcass bruising (OR: 0.997, CI: 0.996 to 0.998; P < 0.0001). Longer lairage was associated with increased odds of DC (P = 0.0415), with each 60-min increase in duration having an OR of 1.034 (CI: 1.001 to 1.068). The results demonstrate the importance of truck arrival management (i.e., scheduling, prioritizing unloading) on mobility. Focusing on lairage management (i.e., density and time) may provide some opportunities to improve meat quality.

Geriatric Voice: Distinctive Clinical Profiles of Working Seniors in a Tertiary Laryngology Clinic

ObjectivesVoice disorders have been reported in up to 47% of the geriatric population. Few studies have assessed dysphonia in the elderly, with none examining vocal function in working seniors. This study aims to profile the clinical characteristics of individuals aged 65 years and older, comparing working and non-working seniors. Study DesignRetrospective cohort study. MethodsMedical charts were reviewed for patients aged 65 years and older referred to the McGill University Health Centre Laryngology Clinic (January 2018 to March 2020). Variables studied included sociodemographics, comorbidities, voice complaints, lifestyle factors, diagnoses, voice acoustics [maximum phonation time (MPT), S/Z ratio, F0], GRBAS scale, VHI-10, RSI, and treatment modalities. ResultsA total of 267 patients were included, with a mean (SD) age of 74.2 (6.6) years and 61% of female patients. Occupation information was available in 148 cases, of which 31.1% were still working and 12.8% were professional voice users. The predominant voice complaint was dysphonia/hoarseness (48.8%) and the most prevalent diagnosis was presbyphonia (31.8%). The mean (SD) VHI-10 and RSI scores were 19.8 (9.0) and 18.7 (9.4) points, respectively. Voice therapy was prescribed for 155 patients (58.7%): 124 attended at least one session with significant post-treatment improvements in the GRBAS and VHI-10 scores (P<0.01). In the univariate analysis, working seniors had a significantly more prevalent singing habit (P=0.04) and laryngopharyngeal reflux diagnosis (P=0.01), displaying a significantly longer MPT (P<0.001) and lower G-B-S scores (P<0.05). After adjusting mean differences, only MPT and the G-S scores were significantly different between both groups. ConclusionsThe predominant diagnosis in elders with voice complaints was presbyphonia. Voice therapy proved effective in improving the VHI-10 and GRBAS scores for geriatric patients. Almost one-third of seniors remained in the workforce, demonstrating superior vocal profiles, specifically in the MPT and the grade and strain of perceptual voice quality.