PMID- 38265063
OWN - NLM
STAT- MEDLINE
DCOM- 20240227
LR  - 20240227
IS  - 1364-5528 (Electronic)
IS  - 0003-2654 (Linking)
VI  - 149
IP  - 5
DP  - 2024 Feb 26
TI  - A simple and efficient alginate hydrogel combined with surface-enhanced Raman 
      spectroscopy for quantitative analysis of sodium nitrite in meat products.
PG  - 1518-1526
LID - 10.1039/d3an01771k [doi]
AB  - Sodium nitrite is a commonly used preservative and color protectant in the food 
      industry. Conventional analytical methods are highly susceptible to food matrix 
      interference, time-consuming and costly. In this study, the ion cross-linking 
      method was employed to prepare alginate hydrogel substrates, and phenosafranin 
      was chosen as a single-molecule probe to analyze sodium nitrite. Our 
      investigation centered on elucidating the effects of alginate and cross-linking 
      ion concentrations on Raman signal characteristics. The optimal Raman response 
      was observed in the precursor solution with 1% sodium alginate and 0.1 mol L(-1) 
      cross-linking ions. The relative standard deviations (RSDs) of the feature peaks 
      from the three substrate batches ranged from 1.22% to 16.30%, attesting the 
      robustness and consistency of the substrates. The signal reduction of the 
      substrates after a four-week storage period remained below 10%, indicating that 
      the substrates had good reproducibility and stability. The limits of detection 
      (LODs) for sodium nitrite in extracts from cured meat, luncheon meat, and sliced 
      ham were determined to range from 3.75 mg kg(-1) to 8.11 mg kg(-1), with low 
      interference from the food matrix. The support vector machine algorithm was 
      utilized to train and predict the data, which proved to be more accurate 
      (98.6%-99.8% recovery) than the traditional linear regression model (81.9%-112.7% 
      recovery) in predicting the spiked samples. The application of hydrogel-based 
      surface-enhanced Raman spectroscopy (SERS) substrates for nitrite detection in 
      food, combined with machine learning for regression prediction in data 
      processing, collectively augmented the potential of SERS technology in the field 
      of food analysis.
FAU - Liang, Fengnian
AU  - Liang F
AD  - College of Food Science and Technology, Shanghai Ocean University, Shanghai, 
      201306, China. kqlai@shou.edu.cn.
AD  - Engineering Research Center of Food Thermal - Processing Technology, Shanghai, 
      201306, China.
FAU - Huang, Yiqun
AU  - Huang Y
AD  - School of Food Science and Bioengineering, Changsha University of Science and 
      Technology, Hunan, 410076, China.
FAU - Miao, Junjian
AU  - Miao J
AD  - College of Food Science and Technology, Shanghai Ocean University, Shanghai, 
      201306, China. kqlai@shou.edu.cn.
AD  - Engineering Research Center of Food Thermal - Processing Technology, Shanghai, 
      201306, China.
FAU - Lai, Keqiang
AU  - Lai K
AUID- ORCID: 0000-0001-5462-6285
AD  - College of Food Science and Technology, Shanghai Ocean University, Shanghai, 
      201306, China. kqlai@shou.edu.cn.
AD  - Engineering Research Center of Food Thermal - Processing Technology, Shanghai, 
      201306, China.
LA  - eng
PT  - Journal Article
DEP - 20240226
PL  - England
TA  - Analyst
JT  - The Analyst
JID - 0372652
RN  - M0KG633D4F (Sodium Nitrite)
RN  - 0 (Hydrogels)
SB  - IM
MH  - *Sodium Nitrite/pharmacology
MH  - *Meat Products
MH  - Spectrum Analysis, Raman/methods
MH  - Hydrogels
MH  - Reproducibility of Results
EDAT- 2024/01/24 12:44
MHDA- 2024/02/27 06:44
CRDT- 2024/01/24 08:59
PHST- 2024/02/27 06:44 [medline]
PHST- 2024/01/24 12:44 [pubmed]
PHST- 2024/01/24 08:59 [entrez]
AID - 10.1039/d3an01771k [doi]
PST - epublish
SO  - Analyst. 2024 Feb 26;149(5):1518-1526. doi: 10.1039/d3an01771k.