Subject Experts Research Articles

Comparing answers of ChatGPT and Google Gemini to common questions on benign anal conditions.

Chatbots have been increasingly used as a source of patient education. This study aimed to compare the answers of ChatGPT-4 and Google Gemini to common questions on benign anal conditions in terms of appropriateness, comprehensiveness, and language level. Each chatbot was asked a set of 30 questions on hemorrhoidal disease, anal fissures, and anal fistulas. The responses were assessed for appropriateness, comprehensiveness, and reference provision. The assessments were made by three subject experts who were unaware of the name of the chatbots. The language level of the chatbot answers was assessed using the Flesch-Kincaid Reading Ease score and grade level. Overall, the answers provided by both models were appropriate and comprehensive. The answers of Google Gemini were more appropriate, comprehensive, and supported by references compared with the answers of ChatGPT. In addition, the agreement among the assessors on the appropriateness of Google Gemini answers was higher, attesting to a higher consistency. ChatGPT had a significantly higher Flesh-Kincaid grade level than Google Gemini (12.3 versus 10.6, p = 0.015), but a similar median Flesh-Kincaid Ease score. The answers of Google Gemini to questions on common benign anal conditions were more appropriate and comprehensive, and more often supported with references, than the answers of ChatGPT. The answers of both chatbots were at grade levels higher than the 6th grade level, which may be difficult for nonmedical individuals to comprehend.

Accuracy of ChatGPT 3.5, 4.0, 4o and Gemini in diagnosing oral potentially malignant lesions based on clinical case reports and image recognition.

The accurate and timely diagnosis of oral potentially malignant lesions (OPMLs) is crucial for effective management and prevention of oral cancer. Recent advancements in artificial intelligence technologies indicates its potential to assist in clinical decision-making. Hence, this study was carried out with the aim to evaluate and compare the diagnostic accuracy of ChatGPT 3.5, 4.0, 4o and Gemini in identifying OPMLs. The analysis was carried out using 42 case reports from PubMed, Scopus and Google Scholar and images from two datasets, corresponding to different OPMLs. The reports were inputted separately for text description-based diagnosis in GPT 3.5, 4.0, 4o and Gemini, and for image recognition-based diagnosis in GPT 4o and Gemini. Two subject-matter experts independently reviewed the reports and offered their evaluations. For text-based diagnosis, among LLMs, GPT 4o got the maximum number of correct responses (27/42), followed by GPT 4.0 (20/42), GPT 3.5 (18/42) and Gemini (15/42). In identifying OPMLs based on image, GPT 4o demonstrated better performance than Gemini. There was fair to moderate agreement found between Large Language Models (LLMs) and subject experts. None of the LLMs matched the accuracy of the subject experts in identifying the correct number of lesions. The results point towards cautious optimism with respect to commonly used LLMs in diagnosing OPMLs. While their potential in diagnostic applications is undeniable, their integration should be approached judiciously.

A deep learning model to identify the initial parameters of dissimilar welded joints under impact fracture

Abstract A weldment is the joining of two elements; therefore, they function as a single unit. However, a “defective weldment” refers to a welded structure that is deficient and weak, resulting in failure at the joint. The aim of the study is to find the initial shielded metal arc welding parameters under which two different steels, AISI 4140 and AISI 1018, were welded from fractures obtained by Charpy impact test. The three initial welding parameters are the type of filler material (AWS E8018 and AWS E9018 electrodes), the application or not of heat treatment, and the number of welding passes (2 or 3). To achieve the above, circular specimens have been designed for testing in the impact machine, allowing for the capture of fracture images from which attributes are extracted through a deep learning model. One novel aspect is the proposed methodology for designing circular specimens with a welded joint in the center with a machined notch to induce fracture under impact loading, allowing the weld penetration to be measured with the naked eye. The other innovative aspect is the deep learning model designed from scratch, consisting of 6 feature extraction layers, a flattening layer, and 4 fully connected classification layers; the model provides a $$100\%$$ 100 % identification of the weld parameters, similar to how a fingerprint identifies its owner; it is also independent of subjective expert opinion, and it is efficient, effective, and low cost.

EXPRESS: Sleep supports the consolidation of newly learned statistical concepts.

Within mathematical cognition the development of conceptual knowledge is seen as critical to developing understanding. Sleep has been well established to play a role in the consolidation of newly learned information and schema-based information but has yet to be explored within mathematical cognition. Across three experiments participants (N = 167) were assigned to a sleep or wake group and then viewed lectures on either p-values, t-test, or z-scores. The sleep group watched the lecture at 9pm, completed an immediate recall task to explain the concept, then a second recall task 12h later at 9am. The wake groups watched the lecture at 9am, and completed an immediate recall task then a second recall task 12h later at 9pm. Written responses were then assessed using a comparative judgement task by subject experts. Across all three experiments, results showed that participants in the sleep group retained their knowledge from the immediate recall to 12 hours later, while in the wake group, participants' knowledge declined significantly between sessions. These results suggest that sleep may be involved in an important process of maintaining the information learned from statistical concepts.

Development of Culturally-Based Lift the Flap Book: Early Childhood Sexual Education Media in Support of SDGs

Objective: The purpose of the study is to develop lift-the-flap book a culturally-based series comprising six books designed as educational media to promote early childhood sexual education. Theoretical Framework: The development of sexual education in early childhood is massive in Indonesia. Many researchers are racing to produce educational media as a response to support sexual education for children. Instilling cultural values in promoting sexual education through the lift the flap book can be a first step to shaping the identity and character of children. Method: This study used Research & development as a method with development model ADDIE. Collection data through interview instruments, questionnaires, and psychological scales. Focus group discussion (FGD) with the subject experts, who were kindergarten teachers, was used to complete the feasibility of lift the flap book. Analysis descriptive and quantitative were used as data analysis. Results and Discussion: The results indicate that the six-book series are feasible as a learning media for introducing sexual education to early childhood. Research Implications: This study highlights the development of lift-the-flap book to promote sex education for early childhood, enhance children’s awareness of sexual abuse, provide self-protection knowledge, empower them to understand and protect their body. Originality/Value: This study is the second-year research stage into the lift-the-flap book module I Can Protect Myself on self-protection with a cultural basis, and as products created by the authors and have never been tested for feasibility.

Revolutionizing Cardiology: The Role of Artificial Intelligence in Echocardiography.

Background: Artificial intelligence (AI) in echocardiography represents a transformative advancement in cardiology, addressing longstanding challenges in cardiac diagnostics. Echocardiography has traditionally been limited by operator-dependent variability and subjective interpretation, which impact diagnostic reliability. This study evaluates the role of AI, particularly machine learning (ML), in enhancing the accuracy and consistency of echocardiographic image analysis and its potential to complement clinical expertise. Methods: A comprehensive review of existing literature was conducted to analyze the integration of AI into echocardiography. Key AI functionalities, such as image acquisition, standard view classification, cardiac chamber segmentation, structural quantification, and functional assessment, were assessed. Comparisons with traditional imaging modalities like computed tomography (CT), nuclear imaging, and magnetic resonance imaging (MRI) were also explored. Results: AI algorithms demonstrated expert-level accuracy in diagnosing conditions such as cardiomyopathies while reducing operator variability and enhancing diagnostic consistency. The application of ML was particularly effective in automating image analysis and minimizing human error, addressing the limitations of subjective operator expertise. Conclusions: The integration of AI into echocardiography marks a pivotal shift in cardiovascular diagnostics, offering enhanced accuracy, consistency, and reliability. By addressing operator variability and improving diagnostic performance, AI has the potential to elevate patient care and herald a new era in cardiology.

Defining and Conceptualizing Technology-Facilitated Abuse ("Tech Abuse"): Findings of a Global Delphi Study.

Technology-facilitated abuse (TFA) describes the misuse or repurposing of digital systems to harass, coerce, or abuse. It is a global problem involving both existing and emerging technologies. Despite significant work across research, policy, and practice to understand the issue, the field operates within linguistic, conceptual, and disciplinary silos, inhibiting collaboration. To address this, the present study used the Delphi technique to reach a consensus on TFA conceptualization, definition, terminology, and measurement among subject experts. Following a literature review, a global, cross-disciplinary sample of academics, practitioners, and policymakers (n = 316) reflected on TFA across three survey rounds. The results showed both aligned and opposing perspectives. "Technology" and "facilitated" were the most preferable terms. Still, there was uncertainty regarding the need for additional terminologies to denote the scope of abuse, such as gendered descriptors. Participants had little familiarity with existing TFA measurement tools, with two-thirds unaware of any. Most experts agreed on conceptualizing TFA based on the perpetrator's behavior, the victim's harm and impact, and consent. They also supported an expansive TFA definition, beyond intimate relationships, that can involve groups and communities as perpetrators or targets. However, they were more reluctant to perceive TFA as a distinct abuse form, or one guided by social norms, legal thresholds, or involving child perpetrators. The findings are discussed in the context of the current TFA landscape, along with study limitations and steps to achieve a more unified TFA understanding.

Leading teacher professional learning for system-wide change: the leadership practices of teaching research officers (jiaoyanyuan) in China

ABSTRACT Teacher leaders at the system level are recognised to have the potential to promote teacher professional learning for systemic change. This study focuses on a special type of system-level teacher leader in China, i.e. teaching research officers (TROs, jiaoyanyuan), subject experts who facilitate teacher professional learning at the system level. Drawing on interviews with 19 district-level TROs from Zhejiang Province, the study qualitatively illustrates their leadership practices in leading and sustaining teacher professional learning for teaching improvement. The findings revealed that system-level leaders in China adopted six major leadership strategies, suggesting a hybrid construct of teacher leadership for promoting teacher professional learning. While challenging the paradoxical and segmental view of teacher leadership enactment in the Western context, the study reflects on the impact of TROs on teacher professionalism in China.

Reliability and psychometric validity of the Bengali version of paranormal and supernatural beliefs scale

ABSTRACT Background: The Paranormal and Supernatural Beliefs Scale (PSBS) was developed to assess paranormal and supernatural beliefs in the population. The scale is of particular interest to Indians as these beliefs abound in India. However, India faced a language barrier because it was only available in English. Aim: To evaluate the validity and reliability of the Bengali translation of the PSBS. Materials and Methods: Two linguistic experts well-versed in English and Bengali languages independently prepared two Bengali translations of the PSBS, giving the first two versions of the Bengali scale. The subject experts by consensus prepared version 3 of the scale. Two other bilingual experts back-translated version 3 of the PSBS into the English language resulting in the fourth version. Subject experts addressed differences between the fourth version of the English scale and the original version. Then version 4 was once again translated to Bengali, leading to the fifth version of the scale, which was administered to 30 individuals, who were asked to critique the items. Their suggestions were incorporated, to arrive at the sixth and final version of the Bengali translation. By convenience sampling, 91 volunteers conversant in both the English and Bengali languages were included. The subjects were administered alternately the original English version or the Bengali version of the scale initially. Then after a gap of 14 days, those who initially received the English version filled up the Bengali translation, while those who received the Bengali version filled up the English version of the scale. Results: The original English scale and the Bengali version had a mean (SD) score of 13.97 (±6.84) and 13.78 (±6.76), respectively. The scores were significantly positively correlated (Spearman’s rho = 0.900; P < 0.001). The translated scale showed a high degree of internal consistency and acceptable split-half reliability. A principal component analysis with varimax rotation yielded a one-factor solution. The Bengali translation of the PSBS was easily comprehended by the participants. Results of the Rasch analysis showed the scale had good infit and outfit values. The person’s reliability was good. The Wright map showed a good distribution of the items. Conclusion: The Bengali translation of PSBS is a valid and reliable tool to gauge the supernatural and paranormal beliefs of the Bengali-speaking population of India.

Online Inspection of Packed Cases

This paper presents a novel approach to automate the inspection process of packed agricultural cases using Artificial Intelligence (AI) and image processing techniques. The current manual inspection system, which assesses only 10% of packed cases based on subjective expert judgment, faces limitations such as inefficiency, subjectivity, and an inability to scale. The proposed solution leverages AI models and advanced image processing algorithms to perform real-time, automated inspections of packed cases. The system assesses quality based on predefined parameters like color, ripeness, and uniformity, as per customer specifications. This approach offers numerous benefits, including increased accuracy, scalability, cost reduction, and enhanced customer satisfaction. Keywords— Artificial Intelligence (AI), Image Processing, Online Inspection, Quality Control Automation, Agricultural Products, Real-Time Feedback, Machine Learning, Objectivity in Inspection, Automated Quality Assessment, Case Inspection.

Dental Students' Learning Experience: Artificial Intelligence vs Human Feedback on Assignments.

This study evaluated the effectiveness of an AI-based tool (ChatGPT-4) (AIT) vs a human tutor (HT) in providing feedback on dental students' assignments. A total of 194 answers to two histology questions were assessed by both tutors using the same rubric. Students compared feedback from both tutors and evaluated its accuracy against a standard rubric. Students' perceptions were collected on five dimensions of feedback quality. A subject expert also evaluated feedback provided by the two tutors for 40 randomly selected answers. No significant differences were found in total scores between HT and AIT for one question, but a significant difference was noted for Question 2 and overall scores. Students' perceptions showed no differences regarding understanding mistakes, promoting critical thinking, feedback comprehension, or relevance. However, students felt more comfortable with HT feedback (X2 = 9.01, P < .05). In contrast, expert evaluation highlighted that AIT scored higher in identifying mistakes, with significant differences in clarity (W = 40.5, P < .001) and suggestions for improvement (W = 96.5, P < .001). AIT demonstrates significant potential to complement HT by providing detailed feedback in a shorter timeframe. While students did not perceive differences in feedback quality, expert analysis identified AIT as superior in clarity and suggestions for improvement.

Time for an Update

Abstract: Scientific evidence for many effects tends to be ambiguous. Here we investigated how psychological novices update their preexisting beliefs about psychological effects based on ambiguous scientific evidence. Specifically, we investigated various predictors and systematic patterns of belief updating. Participants were presented a series of fictitious hypotheses, accompanied by a series of fictitious study outcomes. For each hypothesis, we assessed participants’ preexisting beliefs and subjective expertise regarding the topic, as well as their posterior beliefs after presentation of scientific evidence. We found a negative effect of subjective expertise and positive effects of trust in psychological science and number of studies investigating an effect on belief updating. We further found evidence for a belief updating pattern according to which participants weight the outcome of the most recent study stronger than that of previous studies. The results advance our understanding of evidence-based belief updating and provide practical implications for science communication.

Patterns of diversity in biomedical coauthorships: An analysis across authors' ethnicity, gender, age, and expertise.

Multiple studies have linked diversity in scientific collaborations to innovative and impactful research. Here, we explore how different diversity indices-ethnicity, gender, academic age, and topical expertise-interact and thereby influence scientific impact. Leveraging nearly 900,000 biomedical journal articles from PubMed, published in major journals between 1991 and 2014, we investigate the nuanced relationships among these diversity indices and their collective influence on research outcomes. By systematically varying model parametrizations, we assess the robustness of the observed relationships and examine multiple methodological choices. Our findings reveal a consistent pattern of demographic homophily, where scientists tend to collaborate with others who share similar ethnic and gender backgrounds. While each diversity index correlates significantly with impact when considered individually, gender diversity and topical expertise emerge as the strongest positive predictors of impact after accounting for key covariates. However, the association between diversity and impact is moderated by the number of collaborating authors, with larger teams sometimes showing opposite trends due to interactions between the computed diversity indices and team size. Despite this complexity, the practical drivers of scientific impact for an article remain the journal of publication, authors' prior citation rate, and the number of co-authors. On further examining expertise diversity through three separate dimensions: variety, balance, and disparity, our findings indicate that impactful teams balance a wide range of subject matter expertise while maintaining a focused connection on closely related topics. These findings highlight the importance of strategic team composition and underline the significance of team diversity in scientific research.

Improving subsurface structural interpretation in complex geological settings through geophysical imaging and machine learning

This study employs seismic refraction tomography (SRT) and electrical resistivity tomography (ERT) to assess subsurface geological conditions along the proposed Porsgrunn Highway in Norway. The primary objective is to analyze SRT and ERT tomograms to identify subsurface geological structures. However, interpreting tomograms is often limited by smoothed boundaries and reduced resolution. To address these challenges, we apply k-means clustering, a machine learning technique that groups data based on similarities in physical properties, to post-process the geophysical tomograms. This study pioneers the use of k-means clustering to interpret tomograms from SRT and ERT data in complex geological settings. We first evaluate the effectiveness of clustering techniques using numerical modeling for two geological scenarios: a horizontally layered case and a layered case with undulation and a fault structure. Utilizing automated methods (Elbow and Silhouette), we objectively determine the optimal number of clusters for each geophysical tomogram. Subsequently, we compare the performance of the k-means clustering algorithm with subjective expert interpretations and the Laplacian edge detection method. Borehole data validate the clustering results and confirm the effectiveness of optimal cluster selection techniques. The findings of this study demonstrate that k-means clustering significantly enhances the detection of geological structures by establishing clearer boundaries and minimizing noise interference, enabling more accurate fault zone delineation. Compared to traditional edge detection and subjective interpretation methods, k-means clustering offers a systematic and objective approach that improves consistency and reliability across diverse geological settings. Moreover, its automated classification of geophysical data into meaningful clusters enables efficient analysis of large datasets. This study underscores the value of integrating machine learning techniques with geophysical methods such as SRT and ERT to improve interpretability and accurately identify subsurface geological structures, particularly in fault zone identification.

Effectiveness of Educational Intervention Regarding Polycystic Ovarian Syndrome Among Undergraduate Students of Tanahu

Background: PCOS is a complex endocrine disorder characterized by hormonal imbalances, reproductive irregularities, and metabolic disturbances. The overall objective was to find out effectiveness of the educational intervention on knowledge on polycystic ovarian syndrome among undergraduate students. Methods: This true experimental study design was carried out in a selected campus of Tanahun district. The study population was undergraduate students who were 18 years and above. A systematic random sampling technique was adopted to select the sample. Data was collected by a pre-tested self-administered questionnaire before educational intervention. The questionnaire was developed by the researcher herself/himself through consulting subject experts and searching literature. The educational intervention package was developed. After three weeks of intervention, a posttest was conducted. The data was entered on SPSS 21.0 version and the result was analyzed by using descriptive statistics and inferential statistics. Results: The knowledge levels of respondents before and after the intervention, along with a control group show that in the pre-intervention phase, a substantial majority of students (96.6%) had an unsatisfactory knowledge level, while only 3.4% fell into the satisfactory category. Following the intervention, with 100% of students achieving a satisfactory knowledge level. In the control group there is a maintained high percentage of unsatisfactory knowledge pre-intervention (98.3%), with a marginal decrease to 96.6% post-intervention. In a study the intervention group showed a significant increase in knowledge with a median pretest score of 3 rising to 28 posttest. Their change score of 25 was highly significant (p=0.00). The control group had a smaller increase from a pretest score of 0 to 2 posttest, with a change score of 2 and a significant p value indicating the intervention’s effectiveness. Conclusion: The positive outcomes observed in this study underscore the importance of an educational initiative in the broader context of health education for young adults.

Challenges in Technology Integration for Elementary Teachers

Integration of technology in education contributed a great impact in the educational system. This study examines existing literature on the challenges faced by elementary school teachers in the implementation of technology into their classrooms, specifically for young learners aged 5-12 as well as drawing on educational theories to overcome these challenges. The Technological Pedagogical Content Knowledge (TPACK) framework was utilized in identifying gaps encountered by teachers in terms of technology knowledge, pedagogical practices, and subject expertise along with the presented barriers of various theories and case studies such as: technical issues, lack of training, time constraints, limited resources, and concerns regarding appropriate use of technology. Finally, recommendations and possible solutions are also discussed to inform future research and practice.

Islamic Framework for Sustainable Development

Purpose: The primary aim of this study is to address the gap in the current academic literature by proposing a comprehensive framework that integrates Islamic principles with the Sustainable Development Goals (SDGs). Study Design/Methodology/Approach: Utilizing a systematic literature review, this study focuses on scholarly works published from 2012 to 2023, employs thematic analysis, and engages with subject experts to craft a framework that advances global sustainability goals. Findings: This study's framework positions Zakat and ethical finance as key to equitable wealth distribution and sustainable economic growth. Halal and Tayyib principles are shown to support ethical consumption and food security, essential for economic sustainability. Islamic teachings on education and gender equity are identified as drivers for societal well-being and equality, central to social sustainability. Additionally, Islamic environmental principles on water, resource use, and marine life ethics reinforce commitments to ecological sustainability. Research Limitations/Implications: While the study lays down a theoretical framework, it emphasizes the need for empirical testing and validation. The implications call for a cross-disciplinary approach to refine the framework and its application across diverse socio-economic and cultural settings. Practical and Social Implications: This framework serves as a blueprint for policymakers, providing actionable insights to integrate Islamic principles with sustainable development initiatives. It underscores the relevance of Islamic values in crafting policies that are not only economically viable but also socially equitable and environmentally sustainable. Originality/Value: The originality of this study lies in its novel approach to synthesizing Islamic teachings with sustainability objectives, contributing a unique perspective to the body of knowledge. It extends the discourse in Islamic economics to include a holistic consideration of environmental concerns, thus offering a comprehensive and practical framework that aligns with global sustainability agendas.

Respiratory symptoms and pulmonary function in paint industry workers exposed to volatile organic compounds: A systematic review and meta-analysis.

Several epidemiological studies have examined the respiratory consequences of occupational exposure to volatile organic compounds (VOCs). However, their effects on paint industry workers in organised and unorganised occupational sectors vary. The present systematic review and meta-analysis aim at evaluating the respiratory symptoms and pulmonary function of paint industry workers from various occupational sectors exposed to VOCs. Relevant MESH terms were used for literature search in MEDLINE, Scopus, Web of Science, and Google Scholar till August 2023. The articles were independently retrieved and qualified by two reviewers and two subject experts arbitrated reviewer differences to establish relevant article inclusion. The systematic review comprised 23 observational studies that assessed respiratory symptom and pulmonary function tests (PFT) among paint industry worker from various occupational sectors. The meta-analysis included 12 studies on respiratory symptoms and 18 on PFT. Pooled meta-analysis was done using random effect model, and the crude odds of respiratory symptoms such as cough (OR: 2.72, 95% confidence interval [CI]: 1.74 to 4.25), dyspnoea (OR: 3.59, 95% CI: 2.13 to 6.05), nasal/throat irritation (OR: 4.5, 95% CI: 1.7 to 12.1), and wheezing (OR: 2.28, 95% CI: 1.37 to 3.82) were significantly higher among paint industry workers exposed to VOC compared to unexposed population. PFT parameters, such as forced expiratory volume in one second (FEV1) (SMD: -0.88, 95% CI: -1.5 to -0.2) and FEV1/forced vital capacity (FEV1/FVC) (SMD: -0.97, 95% CI: -1.6 to -0.32) were found to be significantly reduced among the paint industry workers. The meta-analysis has helped in generating evidence regarding the effect of VOC on respiratory symptoms and pulmonary function and the strength of the association varied with geographical regions, and the type of occupational sectors. Despite the heterogeneity (I2 > 75%) of studies, statistical power of this analysis was significant. Trial registration: PROSPERO registration number: CRD42022311390.

Unveiling the potential of generative artificial intelligence: a multidimensional journey into the future

PurposeThe launch of ChatGPT has brought the large language model (LLM)-based generative artificial intelligence (GAI) into the spotlight, triggering the interests of various stakeholders to seize the possible opportunities implicated by it. Nevertheless, there are also challenges that the stakeholders should observe when they are considering the potential of GAI. Given this backdrop, this study presents the viewpoints gathered from various subject experts on six identified areas.Design/methodology/approachThrough an expert-based approach, this paper gathers the viewpoints of various subject experts on the identified areas of tourism and hospitality, marketing, retailing, service operations, manufacturing and healthcare.FindingsThe subject experts first share an overview of the use of GAI, followed by the relevant opportunities and challenges in implementing GAI in each identified area. Afterwards, based on the opportunities and challenges, the subject experts propose several research agendas for the stakeholders to consider.Originality/valueThis paper serves as a frontier in exploring the opportunities and challenges implicated by the GAI in six identified areas that this emerging technology would considerably influence. It is believed that the viewpoints offered by the subject experts would enlighten the stakeholders in the identified areas.

A Handheld Colorimeter for Remote and Onsite Recognition of Baking Levels at High Temperature — Pork Floss as a Case Study

AbstractPork floss is a common dried meat product in Asia. The endpoint of the baking process is traditionally determined by subjective human experts and indirect temperature measurements, which can often result in unstandardized production. Current colorimeters are unavailable for onsite measurement due to limitations associated with contact measurement and environmental temperature. Instead of the abovementioned human experts and tabletop colorimeters, a handheld colorimeter was built based on the expertise of human specialists and utilizing a tabletop colorimeter and other optical steps. First, the selected samples were used to determine the upper and lower limits distinguishing light, medium, and heavy baking levels by using a tabletop colorimeter. Second, independent light sources and spectrometers were utilized to choose the characteristic and reference wavelengths at 450 and 830 nm, separately. Third, the handheld colorimeter, instead of human expert observation, was designed with functions such as distance sensing and Internet of Things capabilities. The baked index was derived from the calibration reflection and established statistical models. Here, the calibration reflection was defined by the normalized intensity at 450 nm relative to 830 nm, and statistical models were founded from the determined samples of upper and lower limits at 95–700 mm. The developed handheld colorimeter demonstrated high agreement rates of 96.84% and 93.86% in separate comparisons with tabletop colorimeters and human experts, respectively. This work indicated the accurate and stable recognition of samples within two limits and overall. Field validation confirmed the performance of remote, economic, and onsite recognition against environmental temperature and noise.