Quantifying Hidden Salt for Precision Healthcare: Sodium Assessment via Joint-Factor Retrieval and Chain-of-Thought Inference

Mingyu Huang; Weiqing Min; Yuehui Fang; Yuna He; Shuqiang Jiang

cs.CLAug 26, 2026

Quantifying Hidden Salt for Precision Healthcare: Sodium Assessment via Joint-Factor Retrieval and Chain-of-Thought Inference

Authors: Mingyu Huang, Weiqing Min, Yuehui Fang, Yuna He, Shuqiang Jiang

Organizations: State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China. · University of Chinese Academy of Sciences, Beijing, China. · National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, China.

Abstract

Precision healthcare, particularly for conditions like hypertension and cardiovascular disease, necessitates monitoring of dietary sodium intake. However, tracking this is hindered by the prevalence of hidden salt in cooking, such as sodium in soy sauce and ketchup. While recipes offer a valuable data source for dietary analysis, sodium-rich seasonings are frequently omitted or described ambiguously in instructions. To solve this issue, we propose SALT, a Sodium Assessing & Level Tracking framework adopting an RAG framework to assess sodium content in recipes. Our framework first introduces a Joint-Factor Embedding Retrieval module to locate similar recipes with specified sodium content for addressing the lack of contextual references. These retrieved samples provide contexts for subsequent inference. Then we design a structured 4-hop Chain-of-Thought inference module to refine the vague estimation from language models through a multi-step sodium estimation. To facilitate our study, we further construct a recipe dataset SALT54k with $54,151$ entries labeled with sodium quantities across $11$ common seasonings. Results on SALT54k demonstrate that our method achieves state-of-the-art performance in sodium estimation. Additional real-world validations confirm the effectiveness of our method, demonstrating its potential as a practical solution for AI-assisted precision healthcare.

Figures & tables

Figure 1: Diagram of the sodium intake impacting health and a failure attempt to assess sodium content due to an ambiguous recipe.

Figure 2: The system framework for the sodium assessing & level tracking

Figure 3: An illustration of the 4-hop CoT framework. S , CS and F respectively denotes the set of sodium-rich seasonings, cooking styles and flavors, respectively.

Figure 4: Distribution of SALT54k dataset. (a), (b), (c), and (d) present the distribution among different cooking scenarios, primary flavors, recipe regions, and recipe text length. (e) presents a nested pie chart where the inner and the outer layer respectively represent the distribution of coarse- and fine-grained styles; the dark and light shades of the same color represent the coarse-grained style and subdivided fine-grained style (labeled in Appendix B ). In (f), various colors denote $32$ distinct ingredients. The size of the blocks in (e) and (f) represents the recipe amount.

Figure 5: Relevance scores radar chart from retrieval evaluation, calculated as the average of three retrieved recipes respective scores, then averaged among the whole testing set. The (

\alpha,\beta,\gamma

) setting in this chart is (0.4,0.4,0.2).

Retrieval Method	Relevance Score	Acc	AwT
Random Retrieval	3.02	26.54	29.90
BM25	8.41	37.18	42.34
bge-m3	13.97	50.69	56.27
bge-m3-recipe	19.36	63.62	71.64

Table 1: Downstream sodium estimation performance under different retrieval methods using the Joint RAG + 4-hop CoT setting. Acc and AwT are reported in %. The best results are bolded.

Settings	Models	Acc	AwT	Salt MAE	Soy Sauce MAE
zero-shot	GLM-5	11.20	19.36	2.90	3.80
	Deepseek-V3.2	15.30	23.96	2.81	3.54
	GPT-5.2	18.18	21.47	2.64	3.77
	Gemini 3.1 Pro	19.52	25.24	2.69	3.77
	Qwen3-Max	16.80	20.25	3.04	3.73
Naive RAG	GLM-5	27.14 \| 30.03	34.39 \| 37.82	2.35 \| 2.27	3.14 \| 3.06

Table 2: Results of different settings on sodium-rich seasonings estimation tasks. The best results are bolded. Acc and AwT are reported in %. The – | – denotes results from 3-shot | 5-shot.

Model	Setting	Acc	AwT	Salt	SS
Llama-3 Chinese	zero-shot	3.82	8.02	3.65	4.81
	Naive 3-shot	14.51	18.65	3.10	3.98
	Joint 3-shot	20.04	24.74	2.34	3.34
	SFT	29.53	35.62	1.87	2.19
	Ours	35.47	43.26	1.69	2.11
Qwen3	zero-shot	6.79	12.91	3.47	4.31

Table 3: Results of locally deployed Llama-3-Chinese and Qwen3-8B models under zero-shot, Naive 3-shot RAG, Joint 3-shot RAG, full parameter supervised fine tuned (SFT), and 3-shot + 4-hop CoT (Ours) settings. SS stands for soy sauce. Acc and AwT are reported in %. Salt and soy sauce are reported as MAE. The best results are bolded.

Retrieval Setting	CoT Setting	GLM-5	Deepseek-V3.2	GPT-5.2	Gemini 3.1 Pro	Qwen3-Max
$\boldsymbol{\times}$	$\boldsymbol{\times}$	11.20	15.30	18.18	19.52	16.80
Random	$\boldsymbol{\times}$	16.38	19.41	21.50	22.15	21.83
Random	4-hop	18.29	20.91	24.08	26.54	23.06
Joint	$\boldsymbol{\times}$	34.21	42.55	47.95	54.89	41.17
Joint	Step-by-step	41.76	46.60	51.49	55.91	43.67
Joint	4-hop	56.40	53.96	57.33	63.62	50.75

Table 4: Ablation results on sodium-rich seasonings estimation tasks. The best results are bolded. Results are reported as accuracy in %. The None Retrieval + None CoT, Joint Retrieval + None CoT, Joint Retrieval + 4-hop CoT setting is respectively identical to zero-shot, few-shot, 4-hop CoT + few-shot setting in the Table 2 .

Model	Beijing		Shanghai		Guangzhou
Model	ZS	Ours	ZS	Ours	ZS	Ours
GLM-5	4.66	3.46	5.79	4.06	3.96	3.01
Deepseek-V3.2	4.92	3.58	5.21	3.84	3.86	2.64
GPT-5.2	4.06	2.89	4.69	3.46	3.24	2.09
Gemini 3.1 Pro	4.18	2.55	4.56	3.13	2.93	1.63
Qwen3-Max	3.96	2.30	4.12	2.56	2.73	1.18

Table 5: Results from real-world validation experiments, reported as total sodium MAE (g). ZS indicates zero-shot settings

Model	Acc	AwT
BERT-base-Chinese-Reg	27.35	31.73
bge-m3-recipe-Reg	30.81	38.02
Ours (Gemini 3.1 Pro, 3-shot)	63.62	71.64

Table 6: Comparison with supervised regression baselines on the testing set. Regression models take the concatenated recipe name, ingredients, and cooking steps as input and predict an 11-dimensional seasoning quantity vector. Acc and AwT are reported in %. The best results are bolded.

Appendix figures & tables6 assets

Supplementary material from the paper’s appendix.

Appendix

Raw Expression Type	Example	Normalization Rule
Explicit Solid Quantity	5g Salt; 1 Tsp MSG	Convert to Teaspoon
Explicit Liquid Quantity	20ml Soy Sauce; 1 Tbsp Oyster Sauce	Convert to Yablespoon
Household Spoon Expression	1 Small Spoon; Half Spoon	Map to Tsp/Tbsp According to Convention
Ambiguous Expression	Proper Amount; A Little	Excluded from Labeled Ground Truth
Non-target Seasoning	Sugar; Pepper	Not Included in the 11-dimensional Label

Appendix

Table 7: Unit normalization protocol for SALT54k labels. Solid seasonings are normalized into teaspoons, while liquid seasonings are normalized into tablespoons.

Figure 6: Relevance scores from retrieval evaluation, calculated as the average of three retrieved recipes respective scores, then averaged among the whole testing set. The x-axis denotes ( $\alpha,\beta,\gamma$ ) settings as A: (1,0,0), B: (0.5,0.5,0), C: (0.33,0.33,0.33) and D: (0.4,0.4,0.2).

Model	Version	Window	Parameter	Temperature	Top-p
Locally Deployed
Llama3-Chinese	Instruct	8k	8B	1.0	1.0
Qwen3	-	128k	8B	1.0	1.0
API-Invoked
GLM-5	Think	200k	-	1.0	0.95
Deepseek-V3.2	Thinking	128k	-	1.0	1.0

Appendix

Table 8: Overview of Benchmark LLMs. We do not set a maximum token limit across models to ensure complete outputs for the 4-hop CoT process.

Settings	Models	MSG	CE	LSS	DSS
zero-shot	Llama-3-Chinese	0.72	0.47	2.41	1.35
	GLM-5	0.68	0.43	2.23	1.10
	Deepseek-V3.2	0.70	0.40	2.39	1.06
	GPT-5.2	0.68	0.44	2.24	1.14
	Gemini 3.1 Pro	0.64	0.33	1.60	0.87
	Qwen3-Max	0.60	0.40	1.73	0.65

Appendix

Table 9: Additional results of LLMs on sodium-rich seasonings estimation tasks. The best results are bolded. Abbreviations stand for: M ono S odium G lutamate ( MSG ), C hicken E ssence ( CE ), L ight S oy S auce ( LSS ), and D ark S oy S auce ( DSS ). The – | – denotes results from 3-shot | 5-shot.

Settings	Models	OS	CS	YBS	CBS	TS
zero-shot	Llama-3-Chinese	0.88	0.45	0.37	0.56	0.09
	GLM-5	0.86	0.41	0.32	0.55	0.09
	Deepseek-V3.2	0.79	0.40	0.36	0.51	0.09
	GPT-5.2	0.79	0.31	0.37	0.59	0.10
	Gemini 3.1 Pro	0.44	0.21	0.16	0.46	0.06
	Qwen3-Max	0.53	0.33	0.15	0.49	0.06

Appendix

Table 10: Additional results of LLMs on sodium-rich seasonings estimation tasks. The best results are bolded. Abbreviations stand for: O yster S auce ( OS ), C hili S auce ( CS ), Y ellow B ean S auce ( YBS ), C hili B ean S auce ( CBS ), and T omato S auce ( TS ). The – | – denotes results from 3-shot | 5-shot.

Figure 7: Examples provided include the input recipe text, the corresponding labels, and the predicted tuples. The red font denotes the incorrect part of the prediction.

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Department of Computer Science University of the Western Cape Cape Town, South Africa · School of Public Health University of the Western Cape Cape Town, South Africa · Department of Dietetics and Nutrition University of the Western Cape Cape Town, South Africa +1
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Sep 14, 2026J/K move · Enter open · S save

A Conservative OCR-Enabled Workflow for R214 Sodium Screening of South African Packaged Foods

Mayimunah Nagayi, Alice Scaria Khan, Tamryn Frank, Rina Swart+1

Department of Computer Science University of the Western Cape Cape Town, South Africa · School of Public Health University of the Western Cape Cape Town, South Africa · Department of Dietetics and Nutrition University of the Western Cape Cape Town, South Africa+1

Open paper AskPDF arXiv

Using food package images to monitor sodium and salt content against South Africa's R214 sodium limits is challenging when screening decisions require product identity, nutrition facts panel evidence, reporting basis, and category-specific thresholds. This study presents a conservative image-based workflow that combines region detection, optical character recognition (OCR), product identity and sodium evidence extraction, R214 category assignment, deterministic threshold comparison, and independent vision language model comparison. The evaluation used 442 packaged food products and 3 929 full package images from a real-world South African food packaging dataset. A YOLO26s small detector generated 4 195 region crops, and strict post-processing produced one sodium evidence row per product. The integrated workflow produced 290 OUTSIDE R214 SCOPE, 139 REVIEW, seven SCREEN-PASS, and six SCREEN-FAIL outcomes. The independent Qwen2.5-VL 7B vision language model workflow produced 387 OUTSIDE R214 SCOPE, 31 REVIEW, twenty SCREEN-PASS, and four SCREEN-FAIL outcomes. The workflows agreed on exact R214 category assignment for 415 of 442 products (93.9%) and on whether the assigned category was within R214 scope for 416 of 442 products (94.1%). Final screening outcome agreement was 307 out of 442 products, or 69.5%. Manual verification on 60 products showed lower strict outcome agreement than regulated status agreement, while all manual INSUFFICIENT DATA cases were kept out of SCREEN-PASS and SCREEN-FAIL by both automated workflows. The findings show that conservative image-based screening can organise package evidence, identify clear cases, and assign uncertain cases to REVIEW rather than forcing SCREEN-PASS or SCREEN-FAIL decisions.

Abstract

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