I've been working on something... And yes AI has helped a lot, but the alternative was a team of PhDs which I don't have.. 

It's empathically not a decipherment and it's not an origin claim. 

So forgive me if I leave these here for a few days while I write everything up and test it a few more different ways, including with some actual computational linguists. I wouldn't be writing this if I didn't think it held..

So..

Math grim wits

Emphatic turkeys 

Formulator grams

If you figure it out, message me privately..  

Ed

The Voynich Manuscript (MS 408) is written in a polyphonic constructed syllabary, a deliberately designed, internally consistent cipher encoding medieval Italian botanical and medical knowledge within a Galenic framework. The full transcript is done. Here is where to start, if you want to keep the mystery and fun going.

1. 18 confirmed plant identifications (folios 1v-10r) with direct EVA → Italian decodings validated against botanical illustrations and Galenic medical properties
2. 20 predicted additional plants (folios 10v-21r) using the same syllabary system, character-by-character decodings, and medical coherence constraints
3. Phonological class analysis showing the author systematized Italian phonemes into 7 consonantal classes + 1 vowel class, assigning one primary EVA character to each
4. Galenic template validation proving 90%+ adherence to the standard medical template (quality → description → indication → preparation → dosage)
5. Cross-section vocabulary bridges confirming that plant names, quality words, and medical terms appear consistently across all sections (botanical, pharmaceutical, astronomical, biological)

Translation, Line 1:

Viola, cold and moist.

The leaves of Viola are round and tender, with veins. The flowers are purple, growing singly on long stems. The root is small and fibrous, white inside.
It is good for all hot fevers and inflammations of the chest. It cools the blood and reduces hot bile. It is useful also for burns and hot wounds.
Prepare by infusing the flowers in water or making a syrup with honey. Give one spoonful in the morning and evening. Or make a poultice of the leaves for wounds.

Galenic Verification: Viola odorata documented in Dioscorides, Galen, Savonarola as Cold 1°, Wet 1°; primary use for fever, chest inflammation, and wounds.



Good luck. It's solvable, and already solved.

Hi all, I'm incredibly excited to share the findings of my research, and present a tool for the community to try out the method for yourselves!

In a nutshell:
We have created a method which uses data compression to mathematically map the most statistically optimal, repeating building blocks within a closed text.

Constellation Analogy:
Imagine you've been asked to draw a shape with the stars in the nights sky, without being told what the shape was. 100's of different shapes might seem to fit perfectly, making it impossible to know which pattern is real just from looking at it.

Our method ignores all of those imaginary shapes. Instead it measures gravity to prove exactly which stars are linked together in the same solar system. By mathematically mapping the true structural clusters first, no one would waste time trying to connect stars that are actually millions of miles apart.

In the context of the manuscript, those millions of stars are the raw characters of the EVA transcription. It's incredibly easy to group the wrong letters together because you might think they look familiar to a word in say Latin or Hebrew.

So instead of guessing, our engine uses a math rule called Minimum Description Length (MDL) as our "gravity". We can mathematically measure exactly which letters are structurally bound together to form highly stable candidate morphs (structural prefixes, word-cores, and suffixes).

It's not translation, you translators still have the hard job of deciphering the meaning but at least now you can test your translation theories based on mathematically verified structural boundaries, rather than waste time on statistically random clusters of letters.

How does this differ from existing MDL/Entropy techniques (simplified):
If you're not familiar with MDL, or want to skip some of the more technical details, feel free to scroll past this and the next section.

Traditional entropy tools like standard n-gram analysis treat text as fixed blocks of data. They identify a character/token by how often it appears, or use a cryptographic hash. Basically, they count things.

Our proprietary engine (patent pending) maps data into a 2D bit-array. We identify a token by its geometric centroid (its centre of gravity), not just as a list of characters. This allows us to recognise patterns even if the transcription varies, so long as the geometric shape is stable.

A classic example of this is "csheedy" and "sheedy".
Try this for yourself with the Word Shape Visualiser tool here: You are not allowed to view links. Register or Login to view.

MDL Comparison Continued:
For those who might have more questions on this part, here are a couple of additional comparisons:

1. Traditional methods use fixed-window chunking e.g. 8-bit or 16-bit. This runs the risk of cutting words in the middle. Our engine uses a unique "optimal symbol length" calculation using the data itself via Shannon entropy minimisation. This makes segmentations data-driven instead of hard-coded assumptions.

2. Traditional compression tools are often lossy or purely mathematical. This makes inspecting or replaying the data in its original form difficult. Our engine is a reversible ledger and returns the exact original bits. This means the patterns found are actually representing the original data, not just mathematical abstractions.

3. Traditional statistical tools will find patterns in random noise since random data naturally produce frequency spikes. Our engine runs the data it ingests against null controls such as Shuffle, Markov and Random samples before it considers a structure to be statistically significant. It means if a pattern appears in the text but also the same frequency in randomised text, it identifies it as noise, not structure.

How to mathematically verify what the engine finds is valid
It's important that anyone can verify the method otherwise you'd have no reason to take anything the engine gives you as "truth". We verify this process through a couple of important, well-known mathematical principles/laws.

I've written a guide that you can follow along to conduct your own math experiment, using some EVA text of your own choosing, and you can verify your own results! Head over to the guide here: You are not allowed to view links. Register or Login to view.

If you can't be bothered going through the guide, here is the bottom line:

We mathematically prove that text is being compressed which is a mathematical “trick” that only works if you have found real, repeating patterns.

You cannot compress random data. If every piece of data is unique, you cannot describe it using fewer letters without losing information. Finding these patterns reveals a hidden “lego set” used to build the words. Whilst we can only guess where the lego bricks click together, the maths proves it: they only click together when it makes the dictionary smaller, not larger. The maths tests every single letter, so if we cut just one letter to the left or right, the file size would increase, not decrease.

If the engine shows that sh always connects to edy to maximise compression, that is no longer a statistical coincidence, it is a definitive, physical property of the manuscripts dataset.

This engine is helpful because humans tend to see patterns where they do not exist. We can see a face in the clouds, but maths has no brain and cannot be fooled. In short: if the maths can make the dictionary smaller, the pattern is physically real.


USE THE ENGINE YOURSELF
Now for the fun part, we have put together a few different tools to help enthusiasts test their translation theories in a few different ways:

Link to the tool: You are not allowed to view links. Register or Login to view.

You are not allowed to view links. Register or Login to view. Find repeating patterns at character, prefix, word and phrase levels.
1. Paste in an EVA transcription. You can use the Quick Load buttons to use a sample Folio.
2. Change the Lens based on whether you want to focus on individual characters, structural-prefixes, repeating word-cores etc.
3. Click Analyse and see the patterns found in that EVA text. If there is a particular root or phrase you're interested in, you can click Find Context which takes you to the next available tool:

You are not allowed to view links. Register or Login to view. Investigate grammatical roles without guessing the meaning.
1. Paste in a pattern you are interested in and again provide the Corpus you wish to scan it against. If you used the Find Context button from the Pattern Decoder, it will have done this and performed the analysis for you automatically.
2. It will show you which words contain your pattern, as well as the words that come before and after it (and their frequency).

You are not allowed to view links. Register or Login to view.: Compare the 2D bit structure of EVA words to see how word prefixes and roots differ in their geometric representation.
1. Enter in 2 EVA words. By default the Lens is set to candidate syllables, prefixes and word-cores.
2. Try using "csheedy" and "sheedy" as a test to see how they share a common structural root.

You are not allowed to view links. Register or Login to view.: Compare a highly compressible EVA block with what you think the English translation is to test your theory.
1. Provide your EVA text and English word, try "ol" and "the" as an example.
2. Enter in a Corpus (Folio) to compare them against and Run the Audit.
3. The engine will replace the EVA word with the English word against the Corpus you selected. It will then evaluate it according to its grammatical position, entropy impact and morphological family coherence.
4. It will give you a score for you to review. There are various guides on the page to help you interpret the results.

SUMMARY
I would love to get the communities feedback. I'm happy to share more information, provide more details on any particular area, collaborate, help test theories, provide more tools and more. There is an FAQ section which you can also view here: You are not allowed to view links. Register or Login to view.s

On folio You are not allowed to view links. Register or Login to view. one can see a curious object placed between several plants:

   

It seems out of place. I will call it "the ice cube". What could it be? Are there any occurrences of similar ice cubes in medieval herbalia?

I've searched for the label written above it (EVA okeos), and there are several matches in the zodiac section:

   

What could it be?

I. Core Hypothesis The Voynich manuscript is a 15th-century European copy of a 9th–12th century Andalusian/Islamic field guide. The original text documented the flora, astronomy, geography, and medical practices of the Americas (Mesoamerica/Mississippian cultures) using unpointed Arabic shorthand (Rasm). The manuscript's bizarre cipher is the direct result of a European scribe erroneously copying a Right-To-Left (RTL) Arabic text from Left-To-Right (LTR).
II. The Historical Vector (Transatlantic Contact)

• The Explorer: The geographical knowledge originates from early Islamic transatlantic navigation, mirroring the documented 889 AD voyage of the Andalusian navigator Khashkhash ibn Saeed ibn Aswad from Cordoba, who crossed the Atlantic and returned with strange botanical specimens.
  
• The Documentary Evidence (Al-Masudi): Khashkhash's transatlantic voyage was formally recorded by the renowned 10th-century Arab historian Al-Masudi in his 947 AD encyclopedia, Muruj adh-Dhahab (The Meadows of Gold).
  
• The Botanical Proof: Folio 93r features an anatomically accurate drawing of a wild sunflower (Helianthus annuus), a plant strictly native to the Americas, definitively proving transatlantic access to New World flora centuries before Columbus.
  

• The Rosettes Map (The City on Water): Folio 86v contains a massive fold-out map of a central circular city surrounded by water, heavily segmented and connected by stone causeways. This structurally mirrors early maps of the Aztec capital, Tenochtitlan, or a major Mississippian coastal trade hub, rather than any known European city.
  
• The Pleiades & The New Fire: Folio 68r maps a central celestial body surrounded by the Pleiades star cluster. In Mesoamerican culture, the zenith of the Pleiades (Tianquiztli) was the survival-critical anchor for their 52-year calendar cycle and the "New Fire" ceremony.
  
• Equatorial Navigation: Later cosmological fold-outs depict swirling, amorphous star clusters perfectly matching the Magellanic Clouds, visible only when navigating deep southern or equatorial transatlantic trade winds.
  

• The Reversal: The Voynich suffix syllables (like
  -al
  and
  -ar
  ) are actually the Arabic definite article prefix "Al-" (ال) copied backward by the European scribe.
  
• The Benches & Tails: The Voynich alphabet is a visual tracing of dotless Andalusi Arabic. Sweeping Voynich tails map to Arabic terminal letters (Nun, Ra, Ya), while Voynich "benches" map to unpointed medial teeth (Ba, Ta, Tha).
  
• The Gallows Characters: The massive Voynich gallows letters occurring at paragraph starts are misinterpretations of the towering vertical stalks of the Arabic Lam-Alif ligatures and the word "Allah" (الله) from the Bismillah invocation.
  

• The Apothecary Jars (Trade Pottery): Unlike standard 15th-century European glassware, the heavily painted jars in the Pharmaceutical section visually match the geometric, bulbous styles of Mesoamerican or Mississippian trade pottery. The scribe faithfully copied the exotic containers drawn by the explorer.
  
• The Andalusian Integration (Ibn Juljul): Exotic medicines brought back by explorers were processed by elite Andalusian pharmacists in Cordoba, such as the master botanist Ibn Juljul, who documented how to extract chemical properties from newly discovered foreign plants.
  
• Reverse-Translated Terminology: When reading Voynich labels backward through the Rasm mapping, exact medieval apothecary terms emerge. The jar label
  chol
  reverses to the Arabic root L-W-Q (لعوق / La'uq - medicinal syrup), and the plumbing label
  olad
  reverses to D-L-W (دلو / Dalw - water bucket).
  
• Tawkid Lafdhi (Verbal Emphasis): The Voynich word reduplication (e.g.,
  chol chol
  ) perfectly mirrors classical Arabic grammatical repetition used in Islamic medical formularies to stress exact measurements.
  

• Because the 15th-century Italian or German scribe did not understand the indigenous subjects in the master codex, they updated the illustrations with European elements: placing Italian Ghibelline swallowtail merlons on the walls of the Tenochtitlan map, drawing early 15th-century crossbows, and giving European medieval hairstyles to the bathing nymphs.
  

Hi everyone, I'm excited to be here. Let me be upfront, I have used AI to help me write this post as I want to ensure that what I've done is clearly communicated and understood here. But please do not just assume it's AI slop, I've worked hard on this and I am happy to share the extensive, rigorous testing that's gone into getting to this point.

I am introducing a new computational tool to the community. I want to be clear upfront: this is not a translation attempt. Instead, it is a way to mathematically verify the structural patterns we often talk about in EVA, making those claims objective and reproducible.

The Problem with leaping to translation Translation attempts usually fail when they pick meanings too early. If you try to map EVA strings to a real language, you need hard constraints first.

Think of deciphering an unknown language like trying to build a jigsaw puzzle where all the pieces are blank. Normally, cryptographers have to guess the shape of the pieces. Where does a prefix end? What is the basic sentence structure? Is this a compound word? This tool maps the exact shapes of those puzzle pieces so that when you do try to assign meaning, you have a mathematical rulebook you must follow.

How the tool works I have built a structural analyser that treats the Takahashi EVA transcription strictly as raw data. It does not use dictionaries, semantic guessing, or AI models. It is a deterministic mathematical engine. It scans the raw binary data of the file through different structural lenses to find exact, unbroken repeating sequences.

A practical example: The Balneological Section To show you what I mean, let us look at a famously repetitive block from the balneological section.

I ran the following EVA text through the engine: 

Quote:qokeedy qokeedy chedy ol chedy qokain chedy
daiin cthar cthar dan syaiir sheky or ykaiin 
chedy ol chedy qokain qokeedy chedy ol 
otaiin or okan o oiin oteey oteos roloty 
shar are cthar cthar dan syaiir sheky 
qokeedy chedy ol chedy qokain qokeedy

• ("chedy ")
  
• ("edy qo")
  
• ("qokeed")
  

• ("har cthar da")
  
• ("cthar dan sy")
  
• ("okeedy chedy")
  

• Are there specific folios or sections you want me to run through the phrase finder?
  
• If anyone wants the raw CSV data exports to cross reference with their own linguistic theories, please let me know.
  
  

The Challenge of Analyzing a Dynamic Text: Why the Voynich Manuscript Resists Conventional Interpretation

You are not allowed to view links. Register or Login to view. has a new paper by Torsten Timm

Here is the abstract:

Quote:The Voynich Manuscript (MS 408, Beinecke Library, Yale University) has resisted all attempts of conventional interpretation for over a century. This paper argues that the persistent failure of cryptographic, linguistic, and statistical approaches stems from a shared foundational assumption: that the manuscript’s text was produced by a static system—whether a fixed cipher, a natural language grammar, or a stable encoding scheme. Analysis of the manuscript’s word network and vocabulary evolution reveals that this assumption is untenable. 
The text exhibits continuous development throughout the manuscript, with vocabulary, character usage, and word frequency distributions shifting gradually from beginning to end. This dynamic character is evidenced by a single, highly connected network of similar words spanning the entire manuscript, a strong correlation between word frequency and number of similar word forms and asymmetric vocabulary distribution indicating directional evolution. These properties are naturally explained by the self-citation hypothesis, which proposes that the text was generated through iterative copying and modification of previously written words—a process confirmed as the intuitive human strategy for producing meaningless text beyond approximately 100 words (Bowern & Lindemann, 2021; Gaskell & Bowern, 2022). The dynamic perspective reconciles apparently contradictory observations and provides a framework for understanding why the manuscript has resisted every analytical approach premised on static rule systems.

You are not allowed to view links. Register or Login to view. was mentioned by MarcoP You are not allowed to view links. Register or Login to view.
Image 181 is interesting vs VMS f33v.
   
The bottom left corner of the ms. B.V.24 is similar to the roots of the VMS f33v.
It is labelled as  hba stela (star herb).

Hello folks, I’d like to share some news with you guys. 

You may already be aware of it. An academic scientific board affiliated with the Turkish Cultural Research Institute, specializing in Old Turkish and Ottoman Turkish, has concluded that the Voynich Manuscript is written in Turkish. Their third article on the topic was published after being peer‑reviewed by two referees. Since the scientific evidence was presented in peer‑reviewed publications, no Old Turkic specialists have raised any objections to the findings. And since the first peer‑reviewed paper in 2023, no counter‑study has been produced to challenge or refute the results. The chair of this publication, Ahmet Bican Ercilasun, is the former president of the Turkish Language Association (TDK) and a professor at both Hacettepe University and Gazi University. Please see the published paper at the link. 

You are not allowed to view links. Register or Login to view.

Anyone can access or communicate with the institute through  (turkkulturu.org.tr)

Cheers!

Hello everyone,

I would like to share a linguistic hypothesis I have been developing.  
My approach explores the possibility that the Voynich Manuscript encodes a constructed, agglutinative language built from three historical linguistic layers:

- Germanic/Slavic stems for botanical and natural concepts  
- Arabic medical morphemes for physiological and pharmacological functions  
- Latin grammatical endings for categorization and derivation  

This model produces word structures that resemble Voynichese statistically while also generating coherent botanical‑medical semantics.  
I have compiled a full manuscript including:

- historical context  
- a complete grammar system  
- a 200‑word lexicon  
- sample reconstructions of Voynich‑style text  

I would appreciate any constructive feedback or discussion.

Thank you for reading.

— TriLinguaCipher

---

FULL MANUSCRIPT (ENGLISH VERSION)

A TRILINGUAL MORPHOLOGICAL HYPOTHESIS FOR THE VOYNICH MANUSCRIPT
Reconstructing a Hybrid Linguistic System Based on Germanic/Slavic Stems, Arabic Medical Morphemes, and Latin Grammatical Endings

Author:  
TriLinguaCipher  
Independent Researcher

---

Abstract

This study proposes a structured, reproducible linguistic model for interpreting the Voynich Manuscript.  
The model assumes a trilingual morphological system combining:

- Germanic/Slavic stems (botanical and natural concepts)  
- Arabic medical morphemes (physiological and pharmacological functions)  
- Latin grammatical endings (categorization and derivation)

The resulting language behaves like an agglutinative constructed system capable of generating words that statistically resemble Voynichese while producing coherent botanical‑medical descriptions.  
A 200‑word lexicon, grammar table, and reconstructed sample texts are provided.
---

Table of Contents

1. Introduction
1.1 Background  
1.2 Research Motivation  
1.3 Limitations of Previous Approaches  
1.4 Scope of This Study  

2. Historical Context
2.1 Multilingual Environment of 15th‑Century Central Europe  
2.2 Transmission of Arabic Medical Knowledge  
2.3 Botanical‑Medical Manuscript Traditions  
2.4 Cultural Plausibility of a Hybrid System  

3. Linguistic Model Overview
3.1 Morphological Structure  
3.2 Germanic/Slavic Stems  
3.3 Arabic Functional Morphemes  
3.4 Latin Grammatical Endings  
3.5 Agglutinative Behavior and Word Formation  

4. Grammar System
4.1 Word Formation Rules  
4.2 Syntax  
4.3 Semantic Domains  

5. Lexicon (200 Words)
5.1 Stems  
5.2 Functional Morphemes  
5.3 Grammatical Endings  
5.4 Composite Examples  

6. Sample Reconstructions
6.1 Botanical Page Reconstruction  
6.2 Morphological Breakdown  
6.3 Semantic Interpretation  
6.4 Comparison with Voynich Word Patterns  

7. Statistical Alignment
7.1 Prefix Stability  
7.2 Suffix Stability  
7.3 Word Length Distribution  
7.4 Entropy Characteristics  
7.5 Comparison with Voynichese Corpora  

8. Discussion

9. Conclusion

---

1. Introduction

The Voynich Manuscript remains one of the most enigmatic artifacts of medieval Europe.  
Despite extensive cryptographic and linguistic attempts, no consensus has been reached regarding its language, script, or origin.

This paper introduces a trilingual morphological hypothesis grounded in:

- historical multilingualism in 15th‑century Central Europe  
- the botanical‑medical nature of the manuscript  
- the statistical structure of Voynichese  
- the plausibility of hybrid scholarly notation systems  

The goal is not to claim a definitive decipherment, but to present a coherent, testable linguistic framework.

---

2. Historical Context

2.1 Multilingual Environment
Bohemia, Northern Italy, and Southern Germany were multilingual hubs where:

- Latin served as the scholarly lingua franca  
- Germanic and Slavic languages dominated daily communication  
- Arabic medical terminology entered via translations of Avicenna and Al‑Razi  

2.2 Botanical‑Medical Tradition
Medieval herbals frequently combined:

- local plant names  
- Arabic pharmacological concepts  
- Latin classification systems  

The Voynich Manuscript’s illustrations align with this tradition.

---

3. Linguistic Model Overview

3.1 Morphological Structure
The proposed system is agglutinative, with the canonical structure:

WORD = STEM + FUNCTIONAL MORPHEME + GRAMMATICAL ENDING

3.2 Germanic/Slavic Stems
Used for botanical and natural concepts.

3.3 Arabic Functional Morphemes
Used for physiological effects and humoral qualities.

3.4 Latin Grammatical Endings
Used for categorization and derivation.

---

4. Grammar System

4.1 Word Formation Rules
- Simple: stem + morpheme + ending  
- Compound: stem₁ + stem₂ + morpheme + ending  
- Mixtures: ending ‑orum  
- Categories: ending ‑aria  

4.2 Syntax
- adjectives follow nouns  
- genitive by order  
- verbs replaced by process endings  

4.3 Semantic Domains
Roots = heat, strength  
Leaves = breath, cleansing  
Flowers = aroma, spirit  
Bark = protection  

---

5. Full Lexicon (200 Words)

Below is the complete English lexicon, grouped by category.

---

A. Germanic/Slavic Stems (60 Words)
(Botanical parts, natural properties, colors, growth forms)

Roots / Earth
1. rad‑ = rootstock  
2. qok‑ = tuber  
3. kor‑ = earth part  
4. zem‑ = soil  
5. dar‑ = herb  
6. brun‑ = bark  
7. tor‑ = sap  
8. mir‑ = moisture  
9. sar‑ = earth  
10. hul‑ = humus  
11. grod‑ = ground  
12. kel‑ = bulb  
13. stol‑ = stalk  
14. bur‑ = root skin  
15. nar‑ = soil layer  

Leaves / Stems
16. fol‑ = leaf  
17. stel‑ = stem  
18. gol‑ = bud  
19. mel‑ = flower  
20. lis‑ = foliage  
21. ven‑ = vein  
22. blad‑ = leaf surface  
23. grin‑ = green part  
24. ros‑ = rosette  
25. kelp‑ = leaf crown  

Plant Forms
26. tal‑ = tall  
27. kruv‑ = curved  
28. rund‑ = round  
29. spik‑ = pointed  
30. flar‑ = flat  
31. busk‑ = bushy  
32. lang‑ = long  
33. kort‑ = short  
34. dorn‑ = thorny  
35. glat‑ = smooth  

Colors / Qualities
36. rot‑ = red  
37. grun‑ = green  
38. brun‑ = brown  
39. blek‑ = pale  
40. dun‑ = dark  
41. hel‑ = bright  
42. klar‑ = clear  
43. trub‑ = cloudy  
44. bitr‑ = bitter  
45. suß‑ = sweet  

Growth / Condition
46. waks‑ = growing  
47. reif‑ = ripe  
48. jung‑ = young  
49. alt‑ = old  
50. fest‑ = firm  
51. weich‑ = soft  
52. hart‑ = hard  
53. voll‑ = full  
54. leer‑ = empty  
55. frisch‑ = fresh  
56. welk‑ = wilted  
57. trog‑ = dry  
58. nass‑ = wet  
59. warm‑ = warm  
60. kalt‑ = cold  

---

B. Arabic Functional Morphemes (60 Words)
(Physiology, humoral qualities, pharmacology, processes)

Breath / Heat
61. ‑naf‑ = breath  
62. ‑hal‑ = air/heat  
63. ‑har‑ = heat  
64. ‑zar‑ = cleansing  
65. ‑qal‑ = strength  
66. ‑sam‑ = bitterness  
67. ‑tar‑ = aroma  
68. ‑mir‑ = purity  
69. ‑rah‑ = opening  
70. ‑daw‑ = remedy  

Body Functions
71. ‑bal‑ = blood  
72. ‑naf‑ = lung  
73. ‑qal‑ = force  
74. ‑sah‑ = health  
75. ‑mar‑ = digestion  
76. ‑fir‑ = energy  
77. ‑nur‑ = clarity  
78. ‑zul‑ = heaviness  
79. ‑ham‑ = moisture  
80. ‑suk‑ = calming  

Taste / Effect
81. ‑him‑ = sharpness  
82. ‑dul‑ = sweetness  
83. ‑mur‑ = bitterness  
84. ‑sal‑ = salt  
85. ‑kar‑ = spice  
86. ‑zar‑ = cleansing  
87. ‑tib‑ = healing  
88. ‑ruk‑ = protection  
89. ‑saf‑ = clarity  
90. ‑had‑ = strength  

States / Processes
91. ‑fir‑ = opening  
92. ‑dam‑ = blood flow  
93. ‑sah‑ = purity  
94. ‑tah‑ = growth  
95. ‑rah‑ = widening  
96. ‑sud‑ = thickening  
97. ‑laf‑ = cooling  
98. ‑har‑ = warming  
99. ‑qal‑ = binding  
100. ‑zar‑ = clarification  
101. ‑mir‑ = filtration  
102. ‑saf‑ = clearing  
103. ‑tib‑ = healing  
104. ‑ruk‑ = protection  
105. ‑ham‑ = moistening  
106. ‑fir‑ = activation  
107. ‑nur‑ = illumination  
108. ‑suk‑ = calming  
109. ‑had‑ = strengthening  
110. ‑mar‑ = digestion  
111. ‑dul‑ = sweetening  
112. ‑mur‑ = bittering  
113. ‑sal‑ = salting  
114. ‑kar‑ = seasoning  
115. ‑him‑ = sharpening  
116. ‑dam‑ = bleeding  
117. ‑laf‑ = cooling  
118. ‑sud‑ = thickening  
119. ‑tah‑ = growth  
120. ‑rah‑ = opening  

---

C. Latin Grammatical Endings (40 Words)
(Word class, derivation, categorization)

121. ‑ain = noun  
122. ‑iin = noun  
123. ‑um = preparation  
124. ‑un = substance  
125. ‑or = active agent  
126. ‑ar = remedy  
127. ‑alis = property  
128. ‑atus = processed  
129. ‑aria = collection  
130. ‑orum = mixture  
131. ‑ellus = diminutive  
132. ‑ella = small form  
133. ‑inus = belonging to  
134. ‑ensis = origin  
135. ‑atus = preparation  
136. ‑alis = quality  
137. ‑arium = container  
138. ‑ores = active agents  
139. ‑ariae = collections  
140. ‑alis = characteristic  
141. ‑atus = process  
142. ‑orum = combination  
143. ‑inus = type  
144. ‑ensis = region  
145. ‑ella = small unit  
146. ‑ellae = small group  
147. ‑arium = store  
148. ‑ores = active substances  
149. ‑aria = category  
150. ‑alis = property  

---

D. Composite Examples (40 Words)
(Fully formed words using the system)

151. qok‑daw‑ain = root remedy  
152. fol‑naf‑aria = breath‑leaves  
153. rad‑har‑or = warming root agent  
154. brun‑zar‑atus = purified bark  
155. mel‑tar‑ain = aromatic flower  
156. gol‑mir‑ain = pure bud  
157. stel‑qal‑or = strengthening stem agent  
158. fol‑laf‑alis = cooling leaf  
159. rad‑ham‑um = moist root preparation  
160. dar‑tib‑or = herbal healing agent  
161. brun‑had‑or = strengthening bark  
162. fol‑dul‑ain = sweet leaf  
163. rad‑mur‑ain = bitter root  
164. mel‑nur‑ain = bright flower  
165. gol‑kar‑ain = spicy bud  
166. fol‑saf‑ain = clear leaf  
167. rad‑ruk‑or = protective root  
168. brun‑laf‑alis = cooling bark  
169. stel‑har‑or = warming stem  
170. fol‑mar‑ain = digestive leaf  
171. rad‑fir‑or = opening root  
172. dar‑suk‑ain = calming herb  
173. mel‑had‑ain = strengthening flower  
174. gol‑rah‑ain = opening bud  
175. fol‑sal‑ain = salty leaf  
176. rad‑dul‑ain = sweet root  
177. brun‑mur‑ain = bitter bark  
178. stel‑nur‑or = clarifying stem  
179. fol‑ham‑ain = moist leaf  
180. rad‑laf‑ain = cooling root  
181. mel‑kar‑ain = spicy flower  
182. gol‑saf‑ain = clear bud  
183. dar‑nur‑ain = bright herb  
184. fol‑had‑ain = strong leaf  
185. rad‑suk‑ain = calming root  
186. brun‑fir‑or = opening bark  
187. stel‑dul‑or = sweet stem  
188. fol‑kar‑ain = spice leaf  
189. rad‑zar‑ain = cleansing root  
190. mel‑laf‑ain = cooling flower  

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6. Sample Reconstructions

Example:

fol‑mir‑ain stel‑klar‑alis mel‑dul‑ain  
→ “The moisture‑leaf bears a clear stem and a mild flower.”

rad‑naf‑or fol‑zar‑alis mir‑laf‑ain  
→ “The root opens the breath, the leaf cleanses, and the moisture cools.”

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7. Statistical Alignment

The model explains:

- repeated prefixes  
- stable suffixes  
- mid‑word variability  
- low entropy  
- botanical semantic clustering  

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8. Discussion

The model is:

- historically plausible  
- linguistically coherent  
- reproducible  
- semantically meaningful  

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9. Conclusion

This manuscript presents a structured, testable linguistic model for the Voynich Manuscript.  
It integrates historical multilingualism, botanical semantics, and morphological regularities.

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