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Page 6 of 15 Lai et al. Microbiome Res Rep 2024;3:21 https://dx.doi.org/10.20517/mrr.2023.76

Table 1. Differences in microbial counts and detection indexes
Indicators MLS-A MR-A MF-A MF-B MF-C MF-D MF-E
LAB counts / Log CFU/mL 8.23 ± 0.05 a 8.54 ± 0.02 b 8.54 ± 0.07 b,c’ 7.78 ± 0.09 a’ 7.90 ± 0.08 b’ 8.56 ± 0.03 d’ 8.56 ± 0.08 d’
Yeast counts/Log CFU/mL 5.04 ± 0.01 c 2.67 ± 0.03 b 2.34 ± 0.08 a,d’ 2.48 ± 0.10 e’ 1.30 ± 0.05 a’ 2.04 ± 0.08 c’ 1.60 ± 0.07 b’
b c a,e’ a’ b’ d’ c’
TA / g/100g 0.60 ± 0.03 0.61 ± 0.01 0.58 ± 0.02 0.29 ± 0.01 0.45 ± 0.01 0.51 ± 0.01 0.50 ± 0.01
TS g/100g 0.53 ± 0.07 a 0.73 ± 0.07 c 0.55 ± 0.08 b,c’ 0.37 ± 0.05 a’ 0.56 ± 0.22 d’ 0.56 ± 0.22 d’ 0.38 ± 0.09 b’
c a b,c’ a’ b’ e’ d’
Lactic acid / g/kg 10.11 ± 0.37 9.13 ± 0.14 9.43 ± 0.17 5.43 ± 0.03 7.41 ± 0.15 10.04 ± 0.03 9.67 ± 0.25
a c b,c’ e’ b’ a’ d’
Acetic acid / g/kg 0.39 ± 0.01 0.95 ± 0.04 0.70 ± 0.03 0.81 ± 0.02 0.63 ± 0.02 0.42 ± 0.02 0.75 ± 0.05
a, b, and c: Means ± standard deviation (n = 3) with different letters within a row are significantly different (P < 0.05) between the different
substrates of ingredients (MLS, MR, and MF). Make a comparison between the different suppliers (MF-A ~ E) and marked as: a’, b’, c’, d’, and e’
(P < 0.05). MLS: Mustard leaf stem; MR: Manjing rhizome; MF: Manjing leaves; LAB: lactic acid bacteria, TA: total acids, TS: total sugars.

mentioning that the phenomenon of sugar filaments was displayed in MF-A, MLS-A, MR-A, MF-C, and
MF-D. TS index was used to characterize filament phenomenon. Results showed that the content of TS was
relatively high in those samples with sugar filament, which made us speculate that the filament appearance
is mainly associated with a specific microbiota no matter what substrates/suppliers of ingredients are.

Differences in organic acids profile
Citric acid, succinic acid, and malic acid were not detected in any of the samples, which could have been
metabolized by microbes to get energy and produce flavor . Lactic acid and acetic acid were the main OAs
[18]
in non-salt Suancai, and their contents are listed in Table 1. The content of lactic acid was roughly the same
among different substrate samples, while the acetic acid content in MLS-A was lower than that in MF-A and
MR-A. Among the same substrate samples, a relatively low content of lactic acid was detected in MF-B and
MF-C, which was consistent with their LAB counts. Interestingly, MF-D showed the highest content of
lactic acid and the lowest content of acetic acid, which implies that a suitable flux of homolactic and
heterolactic fermentation occurred in the Suancai system and was related to LAB strains .
[26]

Non-target metabolomics analysis
Differences in metabolite profiles
A total of 510 metabolites were identified in all samples for the positive mode and 317 metabolites in the
negative mode. PCA was performed on the peaks detected in the experimental and QC samples, and the QC
samples were clustered together on the PCA diagrams [Supplementary Figure 2A] to ensure the quality of
the measured data. There were 809 shared metabolites identified in all samples, and there were no exclusive
metabolites [Supplementary Figure 2B]. In the phytochemical classification of metabolites, the total
proportion of primary (287) and secondary (315) metabolites was 74.13%. Lipids, carbohydrates, and amino
acids and derivatives were the three most abundant primary metabolites [Supplementary Figure 3A],
accounting for 94.43% of the total primary metabolites. The major secondary metabolites were flavonoids,
phenolic acids, terpenoids, organic acids, indoles, and coumarins and their derivatives, all of which
accounted for 89.53% of the total secondary metabolites [Supplementary Figure 3B].

The metabolites were further enriched into corresponding metabolic pathways based on the KEGG database
and annotation information. They mainly involved three biological metabolic pathways - metabolism,
environmental information, and genetic information processing [Supplementary Figure 4A]. Biosynthesis of
various plant secondary metabolites, tyrosine metabolism, and ABC transporters were more active and
involved more metabolites [Supplementary Figure 4B]. In addition, the metabolites were classified using the
HMDS database. The most abundant metabolites were lipids and lipid-like molecules (208), followed by
organoheterocyclic compounds (132) and fatty acyls (124) [Supplementary Figure 4C]. It is worth noting
that the nucleotide metabolism was active, which was probably associated with intracellular nucleotide-