1
|
Thiruvengadam M, Kim JT, Kim WR, Kim JY, Jung BS, Choi HJ, Chi HY, Govindasamy R, Kim SH. Safeguarding Public Health: Advanced Detection of Food Adulteration Using Nanoparticle-Based Sensors. Crit Rev Anal Chem 2024:1-21. [PMID: 39269682 DOI: 10.1080/10408347.2024.2399202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2024]
Abstract
Food adulteration, whether intentional or accidental, poses a significant public health risk. Traditional detection methods often lack the precision required to detect subtle adulterants that can be harmful. Although chromatographic and spectrometric techniques are effective, their high cost and complexity have limited their widespread use. To explore and validate the application of nanoparticle-based sensors for enhancing the detection of food adulteration, focusing on their specificity, sensitivity, and practical utility in the development of resilient food safety systems. This study integrates forensic principles with advanced nanomaterials to create a robust detection framework. Techniques include the development of nanoparticle-based assays designed to improve the detection specificity and sensitivity. In addition, sensor-based technologies, including electronic noses and tongues, have been assessed for their capacity to mimic and enhance human sensory detection, offering objective and reliable results. The use of nanomaterials, including functionalized nanoparticles, has significantly improved the detection of trace amounts of adulterants. Nanoparticle-based sensors demonstrate superior performance in terms of speed, sensitivity, and selectivity compared with traditional methods. Moreover, the integration of these sensors into food safety protocols shows promise for real-time and onsite detection of adulteration. Nanoparticle-based sensors represent a cutting-edge approach for detecting food adulteration, and offer enhanced sensitivity, specificity, and scalability. By integrating forensic principles and nanotechnology, this framework advances the development of more resilient food-safety systems. Future research should focus on optimizing these technologies for widespread application and adapting them to address emerging adulteration threats.
Collapse
Affiliation(s)
- Muthu Thiruvengadam
- Department of Crop Science, College of Sanghuh Life Science, Konkuk University, Seoul, Republic of Korea
| | - Jung-Tae Kim
- Planning and Coordination Division, National Institute of Crop Science, Rural Development Administration (RDA), Jellabuk-do, Republic of Korea
| | - Won-Ryeol Kim
- Department of Crop Science, College of Sanghuh Life Science, Konkuk University, Seoul, Republic of Korea
| | - Ji-Ye Kim
- Department of Crop Science, College of Sanghuh Life Science, Konkuk University, Seoul, Republic of Korea
| | - Bum-Su Jung
- Department of Crop Science, College of Sanghuh Life Science, Konkuk University, Seoul, Republic of Korea
| | - Hee-Jin Choi
- Department of Crop Science, College of Sanghuh Life Science, Konkuk University, Seoul, Republic of Korea
| | - Hee-Youn Chi
- Department of Crop Science, College of Sanghuh Life Science, Konkuk University, Seoul, Republic of Korea
| | - Rajakumar Govindasamy
- Department of Orthodontics, Saveetha Institute of Medical and Technical Sciences, Saveetha Dental College and Hospitals, Saveetha University, Chennai, India
| | - Seung-Hyun Kim
- Department of Crop Science, College of Sanghuh Life Science, Konkuk University, Seoul, Republic of Korea
| |
Collapse
|
2
|
Amoriello T, Ciorba R, Ruggiero G, Amoriello M, Ciccoritti R. A Performance Evaluation of Two Hyperspectral Imaging Systems for the Prediction of Strawberries' Pomological Traits. SENSORS (BASEL, SWITZERLAND) 2023; 24:174. [PMID: 38203035 PMCID: PMC10781302 DOI: 10.3390/s24010174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 12/20/2023] [Accepted: 12/26/2023] [Indexed: 01/12/2024]
Abstract
Pomological traits are the major factors determining the quality and price of fresh fruits. This research was aimed to investigate the feasibility of using two hyperspectral imaging (HSI) systems in the wavelength regions comprising visible to near infrared (VisNIR) (400-1000 nm) and short-wave infrared (SWIR) (935-1720 nm) for predicting four strawberry quality attributes (firmness-FF, total soluble solid content-TSS, titratable acidity-TA, and dry matter-DM). Prediction models were developed based on artificial neural networks (ANN). The entire strawberry VisNIR reflectance spectra resulted in accurate predictions of TSS (R2 = 0.959), DM (R2 = 0.947), and TA (R2 = 0.877), whereas good prediction was observed for FF (R2 = 0.808). As for models from the SWIR system, good correlations were found between each of the physicochemical indices and the spectral information (R2 = 0.924 for DM; R2 = 0.898 for TSS; R2 = 0.953 for TA; R2 = 0.820 for FF). Finally, data fusion demonstrated a higher ability to predict fruit internal quality (R2 = 0.942 for DM; R2 = 0. 981 for TSS; R2 = 0.976 for TA; R2 = 0.951 for FF). The results confirmed the potential of these two HSI systems as a rapid and nondestructive tool for evaluating fruit quality and enhancing the product's marketability.
Collapse
Affiliation(s)
- Tiziana Amoriello
- CREA—Research Centre for Food and Nutrition, Via Ardeatina 546, 00178 Rome, Italy
| | - Roberto Ciorba
- CREA—Research Centre for Olive, Fruit and Citrus Crops, Via di Fioranello 52, 00134 Rome, Italy; (R.C.); (G.R.)
| | - Gaia Ruggiero
- CREA—Research Centre for Olive, Fruit and Citrus Crops, Via di Fioranello 52, 00134 Rome, Italy; (R.C.); (G.R.)
| | - Monica Amoriello
- CREA—Central Administration, Via Archimede 59, 00197 Rome, Italy;
| | - Roberto Ciccoritti
- CREA—Research Centre for Olive, Fruit and Citrus Crops, Via di Fioranello 52, 00134 Rome, Italy; (R.C.); (G.R.)
| |
Collapse
|